R-universe search: clinical

insightsengineering

teal.modules.clinical:'teal' Modules for Standard Clinical Outputs

Provides user-friendly tools for creating and customizing clinical trial reports. By leveraging the 'teal' framework, this package provides 'teal' modules to easily create an interactive panel that allows for seamless adjustments to data presentation, thereby streamlining the creation of detailed and accurate reports.

Maintained by Dawid Kaledkowski. Last updated 16 days ago.

clinical-trials modules nest outputs shiny

50.3 match 34 stars 10.25 score 149 scripts

openanalytics

clinDataReview:Clinical Data Review Tool

Creation of interactive tables, listings and figures ('TLFs') and associated report for exploratory analysis of data in a clinical trial, e.g. for clinical oversight activities. Interactive figures include sunburst, treemap, scatterplot, line plot and barplot of counts data. Interactive tables include table of summary statistics (as counts of adverse events, enrollment table) and listings. Possibility to compare data (summary table or listing) across two data batches/sets. A clinical data review report is created via study-specific configuration files and template 'R Markdown' reports contained in the package.

Maintained by Laure Cougnaud. Last updated 9 months ago.

64.0 match 11 stars 7.10 score 36 scripts

rfhb

ctrdata:Retrieve and Analyze Clinical Trials in Public Registers

A system for querying, retrieving and analyzing protocol- and results-related information on clinical trials from four public registers, the 'European Union Clinical Trials Register' ('EUCTR', <https://www.clinicaltrialsregister.eu/>), 'ClinicalTrials.gov' (<https://clinicaltrials.gov/> and also translating queries the retired classic interface), the 'ISRCTN' (<http://www.isrctn.com/>) and the 'European Union Clinical Trials Information System' ('CTIS', <https://euclinicaltrials.eu/>). Trial information is downloaded, converted and stored in a database ('PostgreSQL', 'SQLite', 'DuckDB' or 'MongoDB'; via package 'nodbi'). Documents in registers associated with trials can also be downloaded. Other functions implement trial concepts canonically across registers, identify deduplicated records, easily find and extract variables (fields) of interest even from complex nested data as used by the registers, merge variables and update queries. The package can be used for meta-analysis and trend-analysis of the design and conduct as well as of the results of clinical trials across registers.

Maintained by Ralf Herold. Last updated 8 hours ago.

clinical-data clinical-research clinical-studies clinical-trials ctgov database duckdb mongodb nodbi postgresql register sqlite studies trial

45.0 match 45 stars 7.92 score 32 scripts

bioc

TCGAbiolinks:TCGAbiolinks: An R/Bioconductor package for integrative analysis with GDC data

The aim of TCGAbiolinks is : i) facilitate the GDC open-access data retrieval, ii) prepare the data using the appropriate pre-processing strategies, iii) provide the means to carry out different standard analyses and iv) to easily reproduce earlier research results. In more detail, the package provides multiple methods for analysis (e.g., differential expression analysis, identifying differentially methylated regions) and methods for visualization (e.g., survival plots, volcano plots, starburst plots) in order to easily develop complete analysis pipelines.

Maintained by Tiago Chedraoui Silva. Last updated 26 days ago.

dnamethylation differentialmethylation generegulation geneexpression methylationarray differentialexpression pathways network sequencing survival software bioc bioconductor gdc integrative-analysis tcga tcga-data tcgabiolinks

17.1 match 305 stars 14.45 score 1.6k scripts 6 dependents

gpaux

Mediana:Clinical Trial Simulations

Provides a general framework for clinical trial simulations based on the Clinical Scenario Evaluation (CSE) approach. The package supports a broad class of data models (including clinical trials with continuous, binary, survival-type and count-type endpoints as well as multivariate outcomes that are based on combinations of different endpoints), analysis strategies and commonly used evaluation criteria.

Maintained by Gautier Paux. Last updated 4 years ago.

biostatistics clinical-trial-simulations clinical-trials simulations

32.3 match 28 stars 6.52 score 39 scripts

bioc

survcomp:Performance Assessment and Comparison for Survival Analysis

Assessment and Comparison for Performance of Risk Prediction (Survival) Models.

Maintained by Benjamin Haibe-Kains. Last updated 5 months ago.

geneexpression differentialexpression visualization cpp

21.7 match 8.46 score 448 scripts 12 dependents

insightsengineering

tern:Create Common TLGs Used in Clinical Trials

Table, Listings, and Graphs (TLG) library for common outputs used in clinical trials.

Maintained by Joe Zhu. Last updated 2 months ago.

clinical-trials graphs listings nest outputs tables

13.6 match 79 stars 12.62 score 186 scripts 9 dependents

ineelhere

clintrialx:Connect and Work with Clinical Trials Data Sources

Are you spending too much time fetching and managing clinical trial data? Struggling with complex queries and bulk data extraction? What if you could simplify this process with just a few lines of code? Introducing 'clintrialx' - Fetch clinical trial data from sources like 'ClinicalTrials.gov' <https://clinicaltrials.gov/> and the 'Clinical Trials Transformation Initiative - Access to Aggregate Content of ClinicalTrials.gov' database <https://aact.ctti-clinicaltrials.org/>, supporting pagination and bulk downloads. Also, you can generate HTML reports based on the data obtained from the sources!

Maintained by Indraneel Chakraborty. Last updated 4 days ago.

aact bioinformatics clinical-data clinical-trials clinicaltrialsgov ctti data data-management medical-informatics r-language trials

28.7 match 15 stars 5.76 score 11 scripts

insightsengineering

teal:Exploratory Web Apps for Analyzing Clinical Trials Data

A 'shiny' based interactive exploration framework for analyzing clinical trials data. 'teal' currently provides a dynamic filtering facility and different data viewers. 'teal' 'shiny' applications are built using standard 'shiny' modules.

Maintained by Dawid Kaledkowski. Last updated 20 days ago.

clinical-trials nest shiny webapp

12.4 match 197 stars 12.68 score 176 scripts 5 dependents

bioc

genefu:Computation of Gene Expression-Based Signatures in Breast Cancer

This package contains functions implementing various tasks usually required by gene expression analysis, especially in breast cancer studies: gene mapping between different microarray platforms, identification of molecular subtypes, implementation of published gene signatures, gene selection, and survival analysis.

Maintained by Benjamin Haibe-Kains. Last updated 4 months ago.

differentialexpression geneexpression visualization clustering classification

19.8 match 7.42 score 193 scripts 3 dependents

therneau

survival:Survival Analysis

Contains the core survival analysis routines, including definition of Surv objects, Kaplan-Meier and Aalen-Johansen (multi-state) curves, Cox models, and parametric accelerated failure time models.

Maintained by Terry M Therneau. Last updated 3 months ago.

6.9 match 400 stars 20.43 score 29k scripts 3.9k dependents

florianstijven

Surrogate:Evaluation of Surrogate Endpoints in Clinical Trials

In a clinical trial, it frequently occurs that the most credible outcome to evaluate the effectiveness of a new therapy (the true endpoint) is difficult to measure. In such a situation, it can be an effective strategy to replace the true endpoint by a (bio)marker that is easier to measure and that allows for a prediction of the treatment effect on the true endpoint (a surrogate endpoint). The package 'Surrogate' allows for an evaluation of the appropriateness of a candidate surrogate endpoint based on the meta-analytic, information-theoretic, and causal-inference frameworks. Part of this software has been developed using funding provided from the European Union's Seventh Framework Programme for research, technological development and demonstration (Grant Agreement no 602552), the Special Research Fund (BOF) of Hasselt University (BOF-number: BOF2OCPO3), GlaxoSmithKline Biologicals, Baekeland Mandaat (HBC.2022.0145), and Johnson & Johnson Innovative Medicine.

Maintained by Wim Van Der Elst. Last updated 17 days ago.

20.8 match 1 stars 6.42 score 133 scripts

alexanderlynl

safestats:Safe Anytime-Valid Inference

Functions to design and apply tests that are anytime valid. The functions can be used to design hypothesis tests in the prospective/randomised control trial setting or in the observational/retrospective setting. The resulting tests remain valid under both optional stopping and optional continuation. The current version includes safe t-tests and safe tests of two proportions. For details on the theory of safe tests, see Grunwald, de Heide and Koolen (2019) "Safe Testing" <arXiv:1906.07801>, for details on safe logrank tests see ter Schure, Perez-Ortiz, Ly and Grunwald (2020) "The Safe Logrank Test: Error Control under Continuous Monitoring with Unlimited Horizon" <arXiv:2011.06931v3> and Turner, Ly and Grunwald (2021) "Safe Tests and Always-Valid Confidence Intervals for contingency tables and beyond" <arXiv:2106.02693> for details on safe contingency table tests.

Maintained by Alexander Ly. Last updated 2 years ago.

evalues hacktoberfest safe-testing statistics

25.3 match 6 stars 5.23 score 14 scripts

rstudio

gt:Easily Create Presentation-Ready Display Tables

Build display tables from tabular data with an easy-to-use set of functions. With its progressive approach, we can construct display tables with a cohesive set of table parts. Table values can be formatted using any of the included formatting functions. Footnotes and cell styles can be precisely added through a location targeting system. The way in which 'gt' handles things for you means that you don't often have to worry about the fine details.

Maintained by Richard Iannone. Last updated 11 days ago.

docx easy-to-use html latex rtf summary-tables

7.0 match 2.1k stars 18.36 score 20k scripts 112 dependents

bxc147

Epi:Statistical Analysis in Epidemiology

Functions for demographic and epidemiological analysis in the Lexis diagram, i.e. register and cohort follow-up data. In particular representation, manipulation, rate estimation and simulation for multistate data - the Lexis suite of functions, which includes interfaces to 'mstate', 'etm' and 'cmprsk' packages. Contains functions for Age-Period-Cohort and Lee-Carter modeling and a function for interval censored data and some useful functions for tabulation and plotting, as well as a number of epidemiological data sets.

Maintained by Bendix Carstensen. Last updated 2 months ago.

12.2 match 4 stars 9.65 score 708 scripts 11 dependents

pharmaverse

pharmaversesdtm:SDTM Test Data for the 'Pharmaverse' Family of Packages

A set of Study Data Tabulation Model (SDTM) datasets from the Clinical Data Interchange Standards Consortium (CDISC) pilot project used for testing and developing Analysis Data Model (ADaM) datasets inside the pharmaverse family of packages. SDTM dataset specifications are described in the CDISC SDTM implementation guide, accessible by creating a free account on <https://www.cdisc.org/>.

Maintained by Edoardo Mancini. Last updated 2 days ago.

15.4 match 15 stars 7.40 score 143 scripts

pharmaverse

admiral:ADaM in R Asset Library

A toolbox for programming Clinical Data Interchange Standards Consortium (CDISC) compliant Analysis Data Model (ADaM) datasets in R. ADaM datasets are a mandatory part of any New Drug or Biologics License Application submitted to the United States Food and Drug Administration (FDA). Analysis derivations are implemented in accordance with the "Analysis Data Model Implementation Guide" (CDISC Analysis Data Model Team, 2021, <https://www.cdisc.org/standards/foundational/adam>).

Maintained by Ben Straub. Last updated 4 days ago.

cdisc clinical-trials open-source

8.0 match 236 stars 13.89 score 486 scripts 4 dependents

keaven

gsDesign:Group Sequential Design

Derives group sequential clinical trial designs and describes their properties. Particular focus on time-to-event, binary, and continuous outcomes. Largely based on methods described in Jennison, Christopher and Turnbull, Bruce W., 2000, "Group Sequential Methods with Applications to Clinical Trials" ISBN: 0-8493-0316-8.

Maintained by Keaven Anderson. Last updated 11 days ago.

biostatistics boundaries clinical-trials design spending-functions

8.3 match 51 stars 13.05 score 338 scripts 5 dependents

msberends

AMR:Antimicrobial Resistance Data Analysis

Functions to simplify and standardise antimicrobial resistance (AMR) data analysis and to work with microbial and antimicrobial properties by using evidence-based methods, as described in <doi:10.18637/jss.v104.i03>.

Maintained by Matthijs S. Berends. Last updated 10 hours ago.

amr antimicrobial-data epidemiology microbiology software

8.8 match 92 stars 11.87 score 182 scripts 6 dependents

rpact-com

rpact:Confirmatory Adaptive Clinical Trial Design and Analysis

Design and analysis of confirmatory adaptive clinical trials with continuous, binary, and survival endpoints according to the methods described in the monograph by Wassmer and Brannath (2016) <doi:10.1007/978-3-319-32562-0>. This includes classical group sequential as well as multi-stage adaptive hypotheses tests that are based on the combination testing principle.

Maintained by Friedrich Pahlke. Last updated 10 days ago.

adaptive-design analysis clinical-trials count-data group-sequential-designs power-calculation sample-size-calculation simulation validated fortran cpp

12.6 match 25 stars 7.98 score 110 scripts 1 dependents

hojsgaard

gRbase:A Package for Graphical Modelling in R

The 'gRbase' package provides graphical modelling features used by e.g. the packages 'gRain', 'gRim' and 'gRc'. 'gRbase' implements graph algorithms including (i) maximum cardinality search (for marked and unmarked graphs). (ii) moralization, (iii) triangulation, (iv) creation of junction tree. 'gRbase' facilitates array operations, 'gRbase' implements functions for testing for conditional independence. 'gRbase' illustrates how hierarchical log-linear models may be implemented and describes concept of graphical meta data. The facilities of the package are documented in the book by Højsgaard, Edwards and Lauritzen (2012, <doi:10.1007/978-1-4614-2299-0>) and in the paper by Dethlefsen and Højsgaard, (2005, <doi:10.18637/jss.v014.i17>). Please see 'citation("gRbase")' for citation details.

Maintained by Søren Højsgaard. Last updated 4 months ago.

openblas cpp

10.8 match 3 stars 9.24 score 241 scripts 20 dependents

thevaachandereng

bayesCT:Simulation and Analysis of Adaptive Bayesian Clinical Trials

Simulation and analysis of Bayesian adaptive clinical trials for binomial, continuous, and time-to-event data types, incorporates historical data and allows early stopping for futility or early success. The package uses novel and efficient Monte Carlo methods for estimating Bayesian posterior probabilities, evaluation of loss to follow up, and imputation of incomplete data. The package has the functionality for dynamically incorporating historical data into the analysis via the power prior or non-informative priors.

Maintained by Thevaa Chandereng. Last updated 5 years ago.

adaptive bayesian-methods bayesian-trial clinical-trials statistical-power

15.7 match 14 stars 6.30 score 36 scripts

graemeleehickey

joineRML:Joint Modelling of Multivariate Longitudinal Data and Time-to-Event Outcomes

Fits the joint model proposed by Henderson and colleagues (2000) <doi:10.1093/biostatistics/1.4.465>, but extended to the case of multiple continuous longitudinal measures. The time-to-event data is modelled using a Cox proportional hazards regression model with time-varying covariates. The multiple longitudinal outcomes are modelled using a multivariate version of the Laird and Ware linear mixed model. The association is captured by a multivariate latent Gaussian process. The model is estimated using a Monte Carlo Expectation Maximization algorithm. This project was funded by the Medical Research Council (Grant number MR/M013227/1).

Maintained by Graeme L. Hickey. Last updated 1 months ago.

armadillo biostatistics clinical-trials cox dynamic joint-models longitudinal-data multivariate-analysis multivariate-data multivariate-longitudinal-data prediction rcpp regression-models statistics survival openblas cpp openmp

11.0 match 30 stars 8.93 score 146 scripts 1 dependents

openanalytics

clinUtils:General Utility Functions for Analysis of Clinical Data

Utility functions to facilitate the import, the reporting and analysis of clinical data. Example datasets in 'SDTM' and 'ADaM' format, containing a subset of patients/domains from the 'CDISC Pilot 01 study' are also available as R datasets to demonstrate the package functionalities.

Maintained by Laure Cougnaud. Last updated 10 months ago.

14.1 match 3 stars 6.78 score 105 scripts 3 dependents

insightsengineering

chevron:Standard TLGs for Clinical Trials Reporting

Provide standard tables, listings, and graphs (TLGs) libraries used in clinical trials. This package implements a structure to reformat the data with 'dunlin', create reporting tables using 'rtables' and 'tern' with standardized input arguments to enable quick generation of standard outputs. In addition, it also provides comprehensive data checks and script generation functionality.

Maintained by Joe Zhu. Last updated 24 days ago.

clinical-trials graphs listings nest reporting tables

11.0 match 12 stars 8.24 score 12 scripts

bioc

cBioPortalData:Exposes and Makes Available Data from the cBioPortal Web Resources

The cBioPortalData R package accesses study datasets from the cBio Cancer Genomics Portal. It accesses the data either from the pre-packaged zip / tar files or from the API interface that was recently implemented by the cBioPortal Data Team. The package can provide data in either tabular format or with MultiAssayExperiment object that uses familiar Bioconductor data representations.

Maintained by Marcel Ramos. Last updated 9 days ago.

software infrastructure thirdpartyclient bioconductor-package nci-itcr u24ca289073

8.7 match 33 stars 10.15 score 147 scripts 4 dependents

ohdsi

PatientLevelPrediction:Develop Clinical Prediction Models Using the Common Data Model

A user friendly way to create patient level prediction models using the Observational Medical Outcomes Partnership Common Data Model. Given a cohort of interest and an outcome of interest, the package can use data in the Common Data Model to build a large set of features. These features can then be used to fit a predictive model with a number of machine learning algorithms. This is further described in Reps (2017) <doi:10.1093/jamia/ocy032>.

Maintained by Egill Fridgeirsson. Last updated 8 days ago.

hades openjdk

7.7 match 190 stars 10.85 score 297 scripts

insightrx

clinPK:Clinical Pharmacokinetics Toolkit

Provides equations commonly used in clinical pharmacokinetics and clinical pharmacology, such as equations for dose individualization, compartmental pharmacokinetics, drug exposure, anthropomorphic calculations, clinical chemistry, and conversion of common clinical parameters. Where possible and relevant, it provides multiple published and peer-reviewed equations within the respective R function.

Maintained by Ron Keizer. Last updated 2 months ago.

clinical-research pharmacokinetics

11.9 match 30 stars 6.72 score 55 scripts

karissawhiting

cbioportalR:Browse and Query Clinical and Genomic Data from cBioPortal

Provides R users with direct access to genomic and clinical data from the 'cBioPortal' web resource via user-friendly functions that wrap 'cBioPortal's' existing API endpoints <https://www.cbioportal.org/api/swagger-ui/index.html>. Users can browse and query genomic data on mutations, copy number alterations and fusions, as well as data on tumor mutational burden ('TMB'), microsatellite instability status ('MSI'), 'FACETS' and select clinical data points (depending on the study). See <https://www.cbioportal.org/> and Gao et al., (2013) <doi:10.1126/scisignal.2004088> for more information on the cBioPortal web resource.

Maintained by Karissa Whiting. Last updated 4 months ago.

11.8 match 21 stars 6.70 score 20 scripts

novartis

RBesT:R Bayesian Evidence Synthesis Tools

Tool-set to support Bayesian evidence synthesis. This includes meta-analysis, (robust) prior derivation from historical data, operating characteristics and analysis (1 and 2 sample cases). Please refer to Weber et al. (2021) <doi:10.18637/jss.v100.i19> for details on applying this package while Neuenschwander et al. (2010) <doi:10.1177/1740774509356002> and Schmidli et al. (2014) <doi:10.1111/biom.12242> explain details on the methodology.

Maintained by Sebastian Weber. Last updated 2 months ago.

bayesian clinical historical-data meta-analysis cpp

10.0 match 22 stars 7.87 score 115 scripts 4 dependents

merck

metalite.ae:Adverse Events Analysis Using 'metalite'

Analyzes adverse events in clinical trials using the 'metalite' data structure. The package simplifies the workflow to create production-ready tables, listings, and figures discussed in the adverse events analysis chapters of "R for Clinical Study Reports and Submission" by Zhang et al. (2022) <https://r4csr.org/>.

Maintained by Yujie Zhao. Last updated 1 months ago.

cdisc clinical-trials metadata

8.3 match 18 stars 9.45 score 31 scripts 2 dependents

merck

metalite:ADaM Metadata Structure

A metadata structure for clinical data analysis and reporting based on Analysis Data Model (ADaM) datasets. The package simplifies clinical analysis and reporting tool development by defining standardized inputs, outputs, and workflow. The package can be used to create analysis and reporting planning grid, mock table, and validated analysis and reporting results based on consistent inputs.

Maintained by Yujie Zhao. Last updated 7 months ago.

cdisc clinical-trials metadata

8.3 match 15 stars 9.01 score 57 scripts 5 dependents

pharmaverse

admiralonco:Oncology Extension Package for ADaM in 'R' Asset Library

Programming oncology specific Clinical Data Interchange Standards Consortium (CDISC) compliant Analysis Data Model (ADaM) datasets in 'R'. ADaM datasets are a mandatory part of any New Drug or Biologics License Application submitted to the United States Food and Drug Administration (FDA). Analysis derivations are implemented in accordance with the "Analysis Data Model Implementation Guide" (CDISC Analysis Data Model Team (2021), <https://www.cdisc.org/standards/foundational/adam>). The package is an extension package of the 'admiral' package.

Maintained by Stefan Bundfuss. Last updated 2 months ago.

8.6 match 32 stars 8.66 score 30 scripts

john-harrold

ruminate:A Pharmacometrics Data Transformation and Analysis Tool

Exploration of pharmacometrics data involves both general tools (transformation and plotting) and specific techniques (non-compartmental analysis). This kind of exploration is generally accomplished by utilizing different packages. The purpose of 'ruminate' is to create a 'shiny' interface to make these tools more broadly available while creating reproducible results.

Maintained by John Harrold. Last updated 6 days ago.

10.5 match 2 stars 7.06 score 84 scripts

pedscience

clinicalsignificance:A Toolbox for Clinical Significance Analyses in Intervention Studies

A clinical significance analysis can be used to determine if an intervention has a meaningful or practical effect for patients. You provide a tidy data set plus a few more metrics and this package will take care of it to make your results publication ready. Accompanying package to Claus et al. <doi:10.18637/jss.v111.i01>.

Maintained by Benedikt Claus. Last updated 3 months ago.

15.5 match 1 stars 4.70 score 20 scripts

annaseffernick

BEAMR:Bootstrap Evaluation of Association Matrices

A bootstrap-based approach to integrate multiple forms of high dimensional genomic data with multiple clinical endpoints. This method is used to find clinically meaningful groups of genomic features, such as genes or pathways. A manuscript describing this method is in preparation.

Maintained by Anna Eames Seffernick. Last updated 8 months ago.

17.9 match 5 stars 4.00 score 1 scripts

shah-in-boots

card:Cardiovascular Applications in Research Data

A collection of cardiovascular research datasets and analytical tools, including methods for cardiovascular procedural data, such as electrocardiography, echocardiography, and catheterization data. Additional methods exist for analysis of procedural billing codes.

Maintained by Anish S. Shah. Last updated 2 months ago.

cardiology research

10.6 match 3 stars 6.73 score 163 scripts

atorus-research

xportr:Utilities to Output CDISC SDTM/ADaM XPT Files

Tools to build CDISC compliant data sets and check for CDISC compliance.

Maintained by Eli Miller. Last updated 3 months ago.

clinical-programmers xpt

7.5 match 43 stars 9.01 score 102 scripts

bioc

GenVisR:Genomic Visualizations in R

Produce highly customizable publication quality graphics for genomic data primarily at the cohort level.

Maintained by Zachary Skidmore. Last updated 5 months ago.

infrastructure datarepresentation classification dnaseq

6.6 match 215 stars 9.87 score 76 scripts

nanxstats

ggsci:Scientific Journal and Sci-Fi Themed Color Palettes for 'ggplot2'

A collection of 'ggplot2' color palettes inspired by plots in scientific journals, data visualization libraries, science fiction movies, and TV shows.

Maintained by Nan Xiao. Last updated 9 months ago.

color-palettes data-visualization ggplot2 ggsci sci-fi scientific-journals visualization

3.6 match 680 stars 18.00 score 26k scripts 438 dependents

bioc

maftools:Summarize, Analyze and Visualize MAF Files

Analyze and visualize Mutation Annotation Format (MAF) files from large scale sequencing studies. This package provides various functions to perform most commonly used analyses in cancer genomics and to create feature rich customizable visualzations with minimal effort.

Maintained by Anand Mayakonda. Last updated 5 months ago.

datarepresentation dnaseq visualization drivermutation variantannotation featureextraction classification somaticmutation sequencing functionalgenomics survival bioinformatics cancer-genome-atlas cancer-genomics genomics maf-files tcga curl bzip2 xz-utils zlib

4.4 match 459 stars 14.63 score 948 scripts 18 dependents

tomdingbiostat

ClinTrialPredict:Predicting and Simulating Clinical Trial with Time-to-Event Endpoint

Predict the course of clinical trial with a time-to-event endpoint for both two-arm and single-arm design. Each of the four primary study design parameters (the expected number of observed events, the number of subjects enrolled, the observation time, and the censoring parameter) can be derived analytically given the other three parameters. And the simulation datasets can be generated based on the design settings.

Maintained by Yang Ding. Last updated 4 months ago.

15.3 match 1 stars 4.18 score

kaneplusplus

preference:2-Stage Preference Trial Design and Analysis

Design and analyze two-stage randomized trials with a continuous outcome measure. The package contains functions to compute the required sample size needed to detect a given preference, treatment, and selection effect; alternatively, the package contains functions that can report the study power given a fixed sample size. Finally, analysis functions are provided to test each effect using either summary data (i.e. means, variances) or raw study data. <doi:10.18637/jss.v094.c02> <doi:10.1002/sim.7830>

Maintained by Michael Kane. Last updated 5 years ago.

clinical-trial-design clinical-trials preference-design

19.3 match 3 stars 3.22 score 11 scripts

graemeleehickey

bayesDP:Implementation of the Bayesian Discount Prior Approach for Clinical Trials

Functions for data augmentation using the Bayesian discount prior method for single arm and two-arm clinical trials, as described in Haddad et al. (2017) <doi:10.1080/10543406.2017.1300907>. The discount power prior methodology was developed in collaboration with the The Medical Device Innovation Consortium (MDIC) Computer Modeling & Simulation Working Group.

Maintained by Graeme L. Hickey. Last updated 3 months ago.

bayesian bayesian-inference bayesian-statistics clinical-trials mdic posterior-predictive posterior-probability prior-distribution openblas cpp

10.9 match 5.56 score 20 scripts 1 dependents

graemeleehickey

goldilocks:Goldilocks Adaptive Trial Designs for Time-to-Event Endpoints

Implements the Goldilocks adaptive trial design for a time to event outcome using a piecewise exponential model and conjugate Gamma prior distributions. The method closely follows the article by Broglio and colleagues <doi:10.1080/10543406.2014.888569>, which allows users to explore the operating characteristics of different trial designs.

Maintained by Graeme L. Hickey. Last updated 2 months ago.

adaptive bayesian bayesian-statistics clinical-trials statistics cpp

12.5 match 7 stars 4.85 score 4 scripts

openbiox

UCSCXenaShiny:Interactive Analysis of UCSC Xena Data

Provides functions and a Shiny application for downloading, analyzing and visualizing datasets from UCSC Xena (<http://xena.ucsc.edu/>), which is a collection of UCSC-hosted public databases such as TCGA, ICGC, TARGET, GTEx, CCLE, and others.

Maintained by Shixiang Wang. Last updated 4 months ago.

cancer-dataset shiny-apps ucsc-xena

7.0 match 96 stars 8.54 score 35 scripts

jessicaweiss

swimplot:Tools for Creating Swimmers Plots using 'ggplot2'

Used for creating swimmers plots with functions to customize the bars, add points, add lines, add text, and add arrows.

Maintained by Jessica Weiss. Last updated 4 years ago.

15.8 match 2 stars 3.76 score 29 scripts

bioc

TCGAutils:TCGA utility functions for data management

A suite of helper functions for checking and manipulating TCGA data including data obtained from the curatedTCGAData experiment package. These functions aim to simplify and make working with TCGA data more manageable. Exported functions include those that import data from flat files into Bioconductor objects, convert row annotations, and identifier translation via the GDC API.

Maintained by Marcel Ramos. Last updated 3 months ago.

software workflowstep preprocessing dataimport bioconductor-package tcga u24ca289073 utilities

6.1 match 26 stars 9.68 score 210 scripts 10 dependents

brockk

escalation:A Modular Approach to Dose-Finding Clinical Trials

Methods for working with dose-finding clinical trials. We provide implementations of many dose-finding clinical trial designs, including the continual reassessment method (CRM) by O'Quigley et al. (1990) <doi:10.2307/2531628>, the toxicity probability interval (TPI) design by Ji et al. (2007) <doi:10.1177/1740774507079442>, the modified TPI (mTPI) design by Ji et al. (2010) <doi:10.1177/1740774510382799>, the Bayesian optimal interval design (BOIN) by Liu & Yuan (2015) <doi:10.1111/rssc.12089>, EffTox by Thall & Cook (2004) <doi:10.1111/j.0006-341X.2004.00218.x>; the design of Wages & Tait (2015) <doi:10.1080/10543406.2014.920873>, and the 3+3 described by Korn et al. (1994) <doi:10.1002/sim.4780131802>. All designs are implemented with a common interface. We also offer optional additional classes to tailor the behaviour of all designs, including avoiding skipping doses, stopping after n patients have been treated at the recommended dose, stopping when a toxicity condition is met, or demanding that n patients are treated before stopping is allowed. By daisy-chaining together these classes using the pipe operator from 'magrittr', it is simple to tailor the behaviour of a dose-finding design so it behaves how the trialist wants. Having provided a flexible interface for specifying designs, we then provide functions to run simulations and calculate dose-paths for future cohorts of patients.

Maintained by Kristian Brock. Last updated 2 months ago.

7.4 match 15 stars 7.91 score 67 scripts

openpharma

simaerep:Find Clinical Trial Sites Under-Reporting Adverse Events

Monitoring of Adverse Event (AE) reporting in clinical trials is important for patient safety. Sites that are under-reporting AEs can be detected using Bootstrap-based simulations that simulate overall AE reporting. Based on the simulation an AE under-reporting probability is assigned to each site in a given trial (Koneswarakantha 2021 <doi:10.1007/s40264-020-01011-5>).

Maintained by Bjoern Koneswarakantha. Last updated 2 months ago.

ae-reporting clinical-trials

10.9 match 22 stars 5.22 score 25 scripts

pharmaverse

admiralvaccine:Vaccine Extension Package for ADaM in 'R' Asset Library

Programming vaccine specific Clinical Data Interchange Standards Consortium (CDISC) compliant Analysis Data Model (ADaM) datasets in 'R'. Flat model is followed as per Center for Biologics Evaluation and Research (CBER) guidelines for creating vaccine specific domains. ADaM datasets are a mandatory part of any New Drug or Biologics License Application submitted to the United States Food and Drug Administration (FDA). Analysis derivations are implemented in accordance with the "Analysis Data Model Implementation Guide" (CDISC Analysis Data Model Team (2021), <https://www.cdisc.org/standards/foundational/adam/adamig-v1-3-release-package>). The package is an extension package of the 'admiral' package.

Maintained by Sukalpo Saha. Last updated 2 months ago.

cdisc

7.4 match 6 stars 7.44 score 23 scripts

massimoaria

dimensionsR:Gathering Bibliographic Records from 'Digital Science Dimensions' Using 'DSL' API

A set of tools to extract bibliographic content from 'Digital Science Dimensions' using 'DSL' API <https://www.dimensions.ai/dimensions-apis/>.

Maintained by Massimo Aria. Last updated 1 years ago.

bibliographic-database bibliography bibliometrics bibliometrix clinical-trials ds-dimensions dsl-api gathering-bibliographic-records grant grants grants-search patents policy-documents publications publications-search

7.5 match 42 stars 7.23 score 7 scripts 3 dependents

ohdsi

OmopSketch:Characterise Tables of an OMOP Common Data Model Instance

Summarises key information in data mapped to the Observational Medical Outcomes Partnership (OMOP) common data model. Assess suitability to perform specific epidemiological studies and explore the different domains to obtain feasibility counts and trends.

Maintained by Cecilia Campanile. Last updated 12 hours ago.

7.1 match 2 stars 7.47 score 16 scripts 1 dependents

bioc

canceR:A Graphical User Interface for accessing and modeling the Cancer Genomics Data of MSKCC

The package is user friendly interface based on the cgdsr and other modeling packages to explore, compare, and analyse all available Cancer Data (Clinical data, Gene Mutation, Gene Methylation, Gene Expression, Protein Phosphorylation, Copy Number Alteration) hosted by the Computational Biology Center at Memorial-Sloan-Kettering Cancer Center (MSKCC).

Maintained by Karim Mezhoud. Last updated 5 months ago.

gui geneexpression clustering go genesetenrichment kegg multiplecomparison cancer cancer-data gene gene-expression gene-methylation gene-mutation gene-sets methylation mskcc mutations tcltk

10.0 match 7 stars 5.25 score 17 scripts

veseshan

clinfun:Clinical Trial Design and Data Analysis Functions

Utilities to make your clinical collaborations easier if not fun. It contains functions for designing studies such as Simon 2-stage and group sequential designs and for data analysis such as Jonckheere-Terpstra test and estimating survival quantiles.

Maintained by Venkatraman E. Seshan. Last updated 1 years ago.

fortran

6.6 match 5 stars 7.86 score 124 scripts 8 dependents

merck

pkglite:Compact Package Representations

A tool, grammar, and standard to represent and exchange R package source code as text files. Converts one or more source packages to a text file and restores the package structures from the file.

Maintained by Nan Xiao. Last updated 4 months ago.

clinical-trials ectd packaging-tool pharmaverse

7.5 match 29 stars 6.75 score 12 scripts

atorus-research

Tplyr:A Traceability Focused Grammar of Clinical Data Summary

A traceability focused tool created to simplify the data manipulation necessary to create clinical summaries.

Maintained by Mike Stackhouse. Last updated 1 years ago.

pharma tables

5.3 match 95 stars 9.49 score 138 scripts 2 dependents

pavlakrotka

NCC:Simulation and Analysis of Platform Trials with Non-Concurrent Controls

Design and analysis of flexible platform trials with non-concurrent controls. Functions for data generation, analysis, visualization and running simulation studies are provided. The implemented analysis methods are described in: Bofill Roig et al. (2022) <doi:10.1186/s12874-022-01683-w>, Saville et al. (2022) <doi:10.1177/17407745221112013> and Schmidli et al. (2014) <doi:10.1111/biom.12242>.

Maintained by Pavla Krotka. Last updated 6 days ago.

clinical-trials platform-trials simulation statistical-inference jags cpp

7.5 match 5 stars 6.64 score 29 scripts

nliulab

AutoScore:An Interpretable Machine Learning-Based Automatic Clinical Score Generator

A novel interpretable machine learning-based framework to automate the development of a clinical scoring model for predefined outcomes. Our novel framework consists of six modules: variable ranking with machine learning, variable transformation, score derivation, model selection, domain knowledge-based score fine-tuning, and performance evaluation.The details are described in our research paper<doi:10.2196/21798>. Users or clinicians could seamlessly generate parsimonious sparse-score risk models (i.e., risk scores), which can be easily implemented and validated in clinical practice. We hope to see its application in various medical case studies.

Maintained by Feng Xie. Last updated 14 days ago.

6.4 match 32 stars 7.70 score 30 scripts

ethan-alt

hdbayes:Bayesian Analysis of Generalized Linear Models with Historical Data

User-friendly functions for leveraging (multiple) historical data set(s) for generalized linear models. The package contains functions for sampling from the posterior distribution of a generalized linear model using the prior induced by the Bayesian hierarchical model, power prior by Ibrahim and Chen (2000) <doi:10.1214/ss/1009212673>, normalized power prior by Duan et al. (2006) <doi:10.1002/env.752>, normalized asymptotic power prior by Ibrahim et al. (2015) <doi:10.1002/sim.6728>, commensurate prior by Hobbs et al. (2011) <doi:10.1111/j.1541-0420.2011.01564.x>, robust meta-analytic-predictive prior by Schmidli et al. (2014) <doi:10.1111/biom.12242>, the latent exchangeability prior by Alt et al. (2023) <doi:10.48550/arXiv.2303.05223>, and a normal (or half-normal) prior. Functions for computing the marginal log-likelihood under each of the implemented priors are also included. The package compiles all the 'CmdStan' models once during installation using the 'instantiate' package.

Maintained by Ethan M. Alt. Last updated 6 days ago.

7.5 match 4 stars 6.23 score 7 scripts

alwinw

epocakir:Clinical Coding of Patients with Kidney Disease

Clinical coding and diagnosis of patients with kidney using clinical practice guidelines. The guidelines used are the evidence-based KDIGO guidelines, see <https://kdigo.org/guidelines/> for more information. This package covers acute kidney injury (AKI), anemia, and chronic kidney disease (CKD).

Maintained by Alwin Wang. Last updated 1 years ago.

kdigo kdigo-guidelines kidney-disease medical

9.3 match 5 stars 5.00 score 5 scripts

humaniverse

geographr:R package for mapping UK geographies

A package to distribute and compute on UK geographical data.

Maintained by Mike Page. Last updated 11 days ago.

6.7 match 38 stars 6.67 score 408 scripts

konstantinryabov

dmtools:Tools for Clinical Data Management

For checking the dataset from EDC(Electronic Data Capture) in clinical trials. 'dmtools' reshape your dataset in a tidy view and check events. You can reshape the dataset and choose your target to check, for example, the laboratory reference range.

Maintained by Konstantin Ryabov. Last updated 2 years ago.

cdisc clinical-data-management laboratory-reference-range-validate

10.3 match 1 stars 4.32 score 14 scripts

simnph

SimNPH:Simulate Non-Proportional Hazards

A toolkit for simulation studies concerning time-to-event endpoints with non-proportional hazards. 'SimNPH' encompasses functions for simulating time-to-event data in various scenarios, simulating different trial designs like fixed-followup, event-driven, and group sequential designs. The package provides functions to calculate the true values of common summary statistics for the implemented scenarios and offers common analysis methods for time-to-event data. Helper functions for running simulations with the 'SimDesign' package and for aggregating and presenting the results are also included. Results of the conducted simulation study are available in the paper: "A Comparison of Statistical Methods for Time-To-Event Analyses in Randomized Controlled Trials Under Non-Proportional Hazards", Klinglmüller et al. (2025) <doi:10.1002/sim.70019>.

Maintained by Tobias Fellinger. Last updated 12 days ago.

clinical-trial-simulations non-proportional-hazards statistical-simulation statistics survival-analysis

6.7 match 6 stars 6.63 score 43 scripts

bioc

cbpManager:Generate, manage, and edit data and metadata files suitable for the import in cBioPortal for Cancer Genomics

This R package provides an R Shiny application that enables the user to generate, manage, and edit data and metadata files suitable for the import in cBioPortal for Cancer Genomics. Create cancer studies and edit its metadata. Upload mutation data of a patient that will be concatenated to the data_mutation_extended.txt file of the study. Create and edit clinical patient data, sample data, and timeline data. Create custom timeline tracks for patients.

Maintained by Arsenij Ustjanzew. Last updated 5 months ago.

immunooncology dataimport datarepresentation gui thirdpartyclient preprocessing visualization cancer-genomics cbioportal clinical-data filegenerator mutation-data patient-data

8.0 match 8 stars 5.51 score 1 scripts

el-meyer

airship:Visualization of Simulated Datasets with Multiple Simulation Input Dimensions

Plots simulation results of clinical trials. Its main feature is allowing users to simultaneously investigate the impact of several simulation input dimensions through dynamic filtering of the simulation results. A more detailed description of the app can be found in Meyer et al. <DOI:10.1016/j.softx.2023.101347> or the vignettes on 'GitHub'.

Maintained by Elias Laurin Meyer. Last updated 3 months ago.

clinical-trials ggplot2 interactive interactive-visualizations simulation visualization

8.0 match 3 stars 5.48 score 2 scripts

drizopoulos

JMbayes2:Extended Joint Models for Longitudinal and Time-to-Event Data

Fit joint models for longitudinal and time-to-event data under the Bayesian approach. Multiple longitudinal outcomes of mixed type (continuous/categorical) and multiple event times (competing risks and multi-state processes) are accommodated. Rizopoulos (2012, ISBN:9781439872864).

Maintained by Dimitris Rizopoulos. Last updated 11 days ago.

competing-risks longitudinal-analysis mixed-models multi-state personalized-medicine precision-medicine prediction-model survival-models openblas cpp openmp

5.3 match 84 stars 8.27 score 264 scripts 2 dependents

insightsengineering

rtables:Reporting Tables

Reporting tables often have structure that goes beyond simple rectangular data. The 'rtables' package provides a framework for declaring complex multi-level tabulations and then applying them to data. This framework models both tabulation and the resulting tables as hierarchical, tree-like objects which support sibling sub-tables, arbitrary splitting or grouping of data in row and column dimensions, cells containing multiple values, and the concept of contextual summary computations. A convenient pipe-able interface is provided for declaring table layouts and the corresponding computations, and then applying them to data.

Maintained by Joe Zhu. Last updated 2 months ago.

pharmaceuticals tables

3.1 match 232 stars 13.65 score 238 scripts 17 dependents

cran

frailtypack:Shared, Joint (Generalized) Frailty Models; Surrogate Endpoints

The following several classes of frailty models using a penalized likelihood estimation on the hazard function but also a parametric estimation can be fit using this R package: 1) A shared frailty model (with gamma or log-normal frailty distribution) and Cox proportional hazard model. Clustered and recurrent survival times can be studied. 2) Additive frailty models for proportional hazard models with two correlated random effects (intercept random effect with random slope). 3) Nested frailty models for hierarchically clustered data (with 2 levels of clustering) by including two iid gamma random effects. 4) Joint frailty models in the context of the joint modelling for recurrent events with terminal event for clustered data or not. A joint frailty model for two semi-competing risks and clustered data is also proposed. 5) Joint general frailty models in the context of the joint modelling for recurrent events with terminal event data with two independent frailty terms. 6) Joint Nested frailty models in the context of the joint modelling for recurrent events with terminal event, for hierarchically clustered data (with two levels of clustering) by including two iid gamma random effects. 7) Multivariate joint frailty models for two types of recurrent events and a terminal event. 8) Joint models for longitudinal data and a terminal event. 9) Trivariate joint models for longitudinal data, recurrent events and a terminal event. 10) Joint frailty models for the validation of surrogate endpoints in multiple randomized clinical trials with failure-time and/or longitudinal endpoints with the possibility to use a mediation analysis model. 11) Conditional and Marginal two-part joint models for longitudinal semicontinuous data and a terminal event. 12) Joint frailty-copula models for the validation of surrogate endpoints in multiple randomized clinical trials with failure-time endpoints. 13) Generalized shared and joint frailty models for recurrent and terminal events. Proportional hazards (PH), additive hazard (AH), proportional odds (PO) and probit models are available in a fully parametric framework. For PH and AH models, it is possible to consider type-varying coefficients and flexible semiparametric hazard function. Prediction values are available (for a terminal event or for a new recurrent event). Left-truncated (not for Joint model), right-censored data, interval-censored data (only for Cox proportional hazard and shared frailty model) and strata are allowed. In each model, the random effects have the gamma or normal distribution. Now, you can also consider time-varying covariates effects in Cox, shared and joint frailty models (1-5). The package includes concordance measures for Cox proportional hazards models and for shared frailty models. 14) Competing Joint Frailty Model: A single type of recurrent event and two terminal events. 15) functions to compute power and sample size for four Gamma-frailty-based designs: Shared Frailty Models, Nested Frailty Models, Joint Frailty Models, and General Joint Frailty Models. Each design includes two primary functions: a power function, which computes power given a specified sample size; and a sample size function, which computes the required sample size to achieve a specified power. Moreover, the package can be used with its shiny application, in a local mode or by following the link below.

Maintained by Virginie Rondeau. Last updated 10 days ago.

fortran openmp

7.6 match 7 stars 5.56 score 1 dependents

elilillyco

rfacts:R Interface to 'FACTS' on Unix-Like Systems

The 'rfacts' package is an R interface to the Fixed and Adaptive Clinical Trial Simulator ('FACTS') on Unix-like systems. It programmatically invokes 'FACTS' to run clinical trial simulations, and it aggregates simulation output data into tidy data frames. These capabilities provide end-to-end automation for large-scale simulation pipelines, and they enhance computational reproducibility. For more information on 'FACTS' itself, please visit <https://www.berryconsultants.com/software/>.

Maintained by William Michael Landau. Last updated 3 years ago.

clinical-trials facts simulation

8.3 match 7 stars 5.02 score 10 scripts

bioc

GenomicDataCommons:NIH / NCI Genomic Data Commons Access

Programmatically access the NIH / NCI Genomic Data Commons RESTful service.

Maintained by Sean Davis. Last updated 1 months ago.

dataimport sequencing api-client bioconductor bioinformatics cancer core-services data-science genomics nci tcga vignette

3.4 match 87 stars 11.94 score 238 scripts 12 dependents

persimune

explainer:Machine Learning Model Explainer

It enables detailed interpretation of complex classification and regression models through Shapley analysis including data-driven characterization of subgroups of individuals. Furthermore, it facilitates multi-measure model evaluation, model fairness, and decision curve analysis. Additionally, it offers enhanced visualizations with interactive elements.

Maintained by Ramtin Zargari Marandi. Last updated 6 months ago.

ai classification clinical-research explainability explainable-ai interpretability machine-learning regression shap statistics

7.5 match 13 stars 5.37 score 12 scripts

rcarragh

c212:Methods for Detecting Safety Signals in Clinical Trials Using Body-Systems (System Organ Classes)

Provides a self-contained set of methods to aid clinical trial safety investigators, statisticians and researchers, in the early detection of adverse events using groupings by body-system or system organ class. This work was supported by the Engineering and Physical Sciences Research Council (UK) (EPSRC) [award reference 1521741] and Frontier Science (Scotland) Ltd. The package title c212 is in reference to the original Engineering and Physical Sciences Research Council (UK) funded project which was named CASE 2/12.

Maintained by Raymond Carragher. Last updated 4 months ago.

cpp

9.9 match 4.06 score 57 scripts

ubcxzhang

DesignCTPB:Design Clinical Trials with Potential Biomarker Effect

Applying 'CUDA' 'GPUs' via 'Numba' for optimal clinical design. It allows the user to utilize a 'reticulate' 'Python' environment and run intensive Monte Carlo simulation to get the optimal cutoff for the clinical design with potential biomarker effect, which can guide the realistic clinical trials.

Maintained by Yitao Lu. Last updated 4 years ago.

10.9 match 1 stars 3.70 score 5 scripts

chstock

DTComPair:Comparison of Binary Diagnostic Tests in a Paired Study Design

Comparison of the accuracy of two binary diagnostic tests in a "paired" study design, i.e. when each test is applied to each subject in the study.

Maintained by Christian Stock. Last updated 5 months ago.

clinical-epidemiology comparative-analysis diagnosis diagnostic-accuracy-studies diagnostic-likelihood-ratio diagnostic-tests medicine predictive-value sensitivity specificity

7.5 match 1 stars 5.07 score 47 scripts

raybaser

PROscorer:Functions to Score Commonly-Used Patient-Reported Outcome (PRO) Measures and Other Psychometric Instruments

An extensible repository of accurate, up-to-date functions to score commonly used patient-reported outcome (PRO), quality of life (QOL), and other psychometric and psychological measures. 'PROscorer', together with the 'PROscorerTools' package, is a system to facilitate the incorporation of PRO measures into research studies and clinical settings in a scientifically rigorous and reproducible manner. These packages and their vignettes are intended to help establish and promote best practices for scoring PRO and PRO-like measures in research. The 'PROscorer' Instrument Descriptions vignette contains descriptions of each instrument scored by 'PROscorer', complete with references. These instrument descriptions are suitable for inclusion in formal study protocol documents, grant proposals, and manuscript Method sections. Each 'PROscorer' function is composed of helper functions from the 'PROscorerTools' package, and users are encouraged to contribute new functions to 'PROscorer'. More scoring functions are currently in development and will be added in future updates.

Maintained by Ray Baser. Last updated 5 months ago.

clinical-trials pros psychometrics qol quality-of-life quality-of-life-questionnaire r-pkg scoring

8.0 match 4 stars 4.75 score 14 scripts

openintrostat

openintro:Datasets and Supplemental Functions from 'OpenIntro' Textbooks and Labs

Supplemental functions and data for 'OpenIntro' resources, which includes open-source textbooks and resources for introductory statistics (<https://www.openintro.org/>). The package contains datasets used in our open-source textbooks along with custom plotting functions for reproducing book figures. Note that many functions and examples include color transparency; some plotting elements may not show up properly (or at all) when run in some versions of Windows operating system.

Maintained by Mine Çetinkaya-Rundel. Last updated 2 months ago.

data openintro

3.3 match 240 stars 11.39 score 6.0k scripts

tidymodels

modeldata:Data Sets Useful for Modeling Examples

Data sets used for demonstrating or testing model-related packages are contained in this package.

Maintained by Max Kuhn. Last updated 5 months ago.

3.5 match 22 stars 10.66 score 2.2k scripts 17 dependents

graemeleehickey

adaptDiag:Bayesian Adaptive Designs for Diagnostic Trials

Simulate clinical trials for diagnostic test devices and evaluate the operating characteristics under an adaptive design with futility assessment determined via the posterior predictive probabilities.

Maintained by Graeme L. Hickey. Last updated 3 months ago.

adaptive bayesian bayesian-statistics clinical-trials diagnostic-tests diagnostics statistics

8.0 match 4 stars 4.60 score 5 scripts

drizopoulos

JMbayes:Joint Modeling of Longitudinal and Time-to-Event Data under a Bayesian Approach

Shared parameter models for the joint modeling of longitudinal and time-to-event data using MCMC; Dimitris Rizopoulos (2016) <doi:10.18637/jss.v072.i07>.

Maintained by Dimitris Rizopoulos. Last updated 4 years ago.

joint-models longitudinal-responses prediction-model survival-analysis openblas cpp openmp jags

5.3 match 60 stars 6.98 score 80 scripts

awconway

spiritR:Template for Clinical Trial Protocol

Contains an R Markdown template for a clinical trial protocol adhering to the SPIRIT statement. The SPIRIT (Standard Protocol Items for Interventional Trials) statement outlines recommendations for a minimum set of elements to be addressed in a clinical trial protocol. Also contains functions to create a xml document from the template and upload it to clinicaltrials.gov<https://www.clinicaltrials.gov/> for trial registration.

Maintained by Aaron Conway. Last updated 6 years ago.

9.2 match 2 stars 4.00 score 2 scripts

insightsengineering

rlistings:Clinical Trial Style Data Readout Listings

Listings are often part of the submission of clinical trial data in regulatory settings. We provide a framework for the specific formatting features often used when displaying large datasets in that context.

Maintained by Joe Zhu. Last updated 2 months ago.

3.4 match 28 stars 10.49 score 44 scripts 9 dependents

pharmaverse

ggsurvfit:Flexible Time-to-Event Figures

Ease the creation of time-to-event (i.e. survival) endpoint figures. The modular functions create figures ready for publication. Each of the functions that add to or modify the figure are written as proper 'ggplot2' geoms or stat methods, allowing the functions from this package to be combined with any function or customization from 'ggplot2' and other 'ggplot2' extension packages.

Maintained by Daniel D. Sjoberg. Last updated 2 months ago.

3.4 match 76 stars 10.50 score 640 scripts 2 dependents

insightsengineering

simIDM:Simulating Oncology Trials using an Illness-Death Model

Based on the illness-death model a large number of clinical trials with oncology endpoints progression-free survival (PFS) and overall survival (OS) can be simulated, see Meller, Beyersmann and Rufibach (2019) <doi:10.1002/sim.8295>. The simulation set-up allows for random and event-driven censoring, an arbitrary number of treatment arms, staggered study entry and drop-out. Exponentially, Weibull and piecewise exponentially distributed survival times can be generated. The correlation between PFS and OS can be calculated.

Maintained by Alexandra Erdmann. Last updated 1 years ago.

multistate-models simulation-engine

5.7 match 13 stars 6.26 score 9 scripts

merck

r2rtf:Easily Create Production-Ready Rich Text Format (RTF) Tables and Figures

Create production-ready Rich Text Format (RTF) tables and figures with flexible format.

Maintained by Benjamin Wang. Last updated 5 days ago.

3.3 match 78 stars 10.82 score 171 scripts 10 dependents

mskcc-epi-bio

PROscorerTools:Tools to Score Patient-Reported Outcome (PRO) and Other Psychometric Measures

Provides a reliable and flexible toolbox to score patient-reported outcome (PRO), Quality of Life (QOL), and other psychometric measures. The guiding philosophy is that scoring errors can be eliminated by using a limited number of well-tested, well-behaved functions to score PRO-like measures. The workhorse of the package is the 'scoreScale' function, which can be used to score most single-scale measures. It can reverse code items that need to be reversed before scoring and pro-rate scores for missing item data. Currently, three different types of scores can be output: summed item scores, mean item scores, and scores scaled to range from 0 to 100. The 'PROscorerTools' functions can be used to write new functions that score more complex measures. In fact, 'PROscorerTools' functions are the building blocks of the scoring functions in the 'PROscorer' package (which is a repository of functions that score specific commonly-used instruments). Users are encouraged to use 'PROscorerTools' to write scoring functions for their favorite PRO-like instruments, and to submit these functions for inclusion in 'PROscorer' (a tutorial vignette will be added soon). The long-term vision for the 'PROscorerTools' and 'PROscorer' packages is to provide an easy-to-use system to facilitate the incorporation of PRO measures into research studies in a scientifically rigorous and reproducible manner. These packages and their vignettes are intended to help establish and promote "best practices" for scoring and describing PRO-like measures in research.

Maintained by Ray Baser. Last updated 1 years ago.

clinical-trials pros psychometrics qol quality-of-life questionnaire survey

7.5 match 2 stars 4.73 score 18 scripts 1 dependents

merck

simtrial:Clinical Trial Simulation

Provides some basic routines for simulating a clinical trial. The primary intent is to provide some tools to generate trial simulations for trials with time to event outcomes. Piecewise exponential failure rates and piecewise constant enrollment rates are the underlying mechanism used to simulate a broad range of scenarios such as those presented in Lin et al. (2020) <doi:10.1080/19466315.2019.1697738>. However, the basic generation of data is done using pipes to allow maximum flexibility for users to meet different needs.

Maintained by Yujie Zhao. Last updated 2 days ago.

cpp

3.8 match 21 stars 9.16 score 52 scripts

boehringer-ingelheim

tipmap:Tipping Point Analysis for Bayesian Dynamic Borrowing

Tipping point analysis for clinical trials that employ Bayesian dynamic borrowing via robust meta-analytic predictive (MAP) priors. Further functions facilitate expert elicitation of a primary weight of the informative component of the robust MAP prior and computation of operating characteristics. Intended use is the planning, analysis and interpretation of extrapolation studies in pediatric drug development, but applicability is generally wider.

Maintained by Christian Stock. Last updated 12 months ago.

bayesian-borrowing bayesian-methods clinical-trial evidence-synthesis extrapolation pediatrics pharmaceutical-development prior-elicitation tipping-point weighting

8.0 match 2 stars 4.38 score 12 scripts

bbecker-bayer

interim:Scheduling Interim Analyses in Clinical Trials

Allows the simulation of the recruitment and both the event and treatment phase of a clinical trial. Based on these simulations, the timing of interim analyses can be assessed.

Maintained by Bastian Becker. Last updated 6 years ago.

23.0 match 1.48 score 7 scripts 1 dependents

julianfaraway

faraway:Datasets and Functions for Books by Julian Faraway

Books are "Linear Models with R" published 1st Ed. August 2004, 2nd Ed. July 2014, 3rd Ed. February 2025 by CRC press, ISBN 9781439887332, and "Extending the Linear Model with R" published by CRC press in 1st Ed. December 2005 and 2nd Ed. March 2016, ISBN 9781584884248 and "Practical Regression and ANOVA in R" contributed documentation on CRAN (now very dated).

Maintained by Julian Faraway. Last updated 1 months ago.

data

3.6 match 29 stars 9.43 score 1.7k scripts 1 dependents

bioc

uncoverappLib:Interactive graphical application for clinical assessment of sequence coverage at the base-pair level

a Shiny application containing a suite of graphical and statistical tools to support clinical assessment of low coverage regions.It displays three web pages each providing a different analysis module: Coverage analysis, calculate AF by allele frequency app and binomial distribution. uncoverAPP provides a statisticl summary of coverage given target file or genes name.

Maintained by Emanuela Iovino. Last updated 5 months ago.

software visualization annotation coverage

7.6 match 3 stars 4.48 score 4 scripts

arminstroebel

atable:Create Tables for Reporting Clinical Trials

Create Tables for Reporting Clinical Trials. Calculates descriptive statistics and hypothesis tests, arranges the results in a table ready for reporting with LaTeX, HTML or Word.

Maintained by Armin Ströbel. Last updated 4 years ago.

7.1 match 9 stars 4.74 score 41 scripts

weiliang

powerSurvEpi:Power and Sample Size Calculation for Survival Analysis of Epidemiological Studies

Functions to calculate power and sample size for testing main effect or interaction effect in the survival analysis of epidemiological studies (non-randomized studies), taking into account the correlation between the covariate of the interest and other covariates. Some calculations also take into account the competing risks and stratified analysis. This package also includes a set of functions to calculate power and sample size for testing main effect in the survival analysis of randomized clinical trials and conditional logistic regression for nested case-control study.

Maintained by Weiliang Qiu. Last updated 4 years ago.

9.0 match 3.72 score 77 scripts 2 dependents

bioc

ClassifyR:A framework for cross-validated classification problems, with applications to differential variability and differential distribution testing

The software formalises a framework for classification and survival model evaluation in R. There are four stages; Data transformation, feature selection, model training, and prediction. The requirements of variable types and variable order are fixed, but specialised variables for functions can also be provided. The framework is wrapped in a driver loop that reproducibly carries out a number of cross-validation schemes. Functions for differential mean, differential variability, and differential distribution are included. Additional functions may be developed by the user, by creating an interface to the framework.

Maintained by Dario Strbenac. Last updated 6 days ago.

classification survival cpp

4.0 match 5 stars 8.36 score 45 scripts 3 dependents

bioc

survClust:Identification Of Clinically Relevant Genomic Subtypes Using Outcome Weighted Learning

survClust is an outcome weighted integrative clustering algorithm used to classify multi-omic samples on their available time to event information. The resulting clusters are cross-validated to avoid over overfitting and output classification of samples that are molecularly distinct and clinically meaningful. It takes in binary (mutation) as well as continuous data (other omic types).

Maintained by Arshi Arora. Last updated 5 months ago.

software clustering survival classification cpp

7.0 match 16 stars 4.74 score 17 scripts

brockk

trialr:Clinical Trial Designs in 'rstan'

A collection of clinical trial designs and methods, implemented in 'rstan' and R, including: the Continual Reassessment Method by O'Quigley et al. (1990) <doi:10.2307/2531628>; EffTox by Thall & Cook (2004) <doi:10.1111/j.0006-341X.2004.00218.x>; the two-parameter logistic method of Neuenschwander, Branson & Sponer (2008) <doi:10.1002/sim.3230>; and the Augmented Binary method by Wason & Seaman (2013) <doi:10.1002/sim.5867>; and more. We provide functions to aid model-fitting and analysis. The 'rstan' implementations may also serve as a cookbook to anyone looking to extend or embellish these models. We hope that this package encourages the use of Bayesian methods in clinical trials. There is a preponderance of early phase trial designs because this is where Bayesian methods are used most. If there is a method you would like implemented, please get in touch.

Maintained by Kristian Brock. Last updated 1 years ago.

cpp

3.9 match 41 stars 8.55 score 106 scripts 3 dependents

begglest

BayesCTDesign:Two Arm Bayesian Clinical Trial Design with and Without Historical Control Data

A set of functions to help clinical trial researchers calculate power and sample size for two-arm Bayesian randomized clinical trials that do or do not incorporate historical control data. At some point during the design process, a clinical trial researcher who is designing a basic two-arm Bayesian randomized clinical trial needs to make decisions about power and sample size within the context of hypothesized treatment effects. Through simulation, the simple_sim() function will estimate power and other user specified clinical trial characteristics at user specified sample sizes given user defined scenarios about treatment effect,control group characteristics, and outcome. If the clinical trial researcher has access to historical control data, then the researcher can design a two-arm Bayesian randomized clinical trial that incorporates the historical data. In such a case, the researcher needs to work through the potential consequences of historical and randomized control differences on trial characteristics, in addition to working through issues regarding power in the context of sample size, treatment effect size, and outcome. If a researcher designs a clinical trial that will incorporate historical control data, the researcher needs the randomized controls to be from the same population as the historical controls. What if this is not the case when the designed trial is implemented? During the design phase, the researcher needs to investigate the negative effects of possible historic/randomized control differences on power, type one error, and other trial characteristics. Using this information, the researcher should design the trial to mitigate these negative effects. Through simulation, the historic_sim() function will estimate power and other user specified clinical trial characteristics at user specified sample sizes given user defined scenarios about historical and randomized control differences as well as treatment effects and outcomes. The results from historic_sim() and simple_sim() can be printed with print_table() and graphed with plot_table() methods. Outcomes considered are Gaussian, Poisson, Bernoulli, Lognormal, Weibull, and Piecewise Exponential. The methods are described in Eggleston et al. (2021) <doi:10.18637/jss.v100.i21>.

Maintained by Barry Eggleston. Last updated 3 years ago.

10.5 match 2 stars 3.15 score 14 scripts

pletschm

aldvmm:Adjusted Limited Dependent Variable Mixture Models

The goal of the package 'aldvmm' is to fit adjusted limited dependent variable mixture models of health state utilities. Adjusted limited dependent variable mixture models are finite mixtures of normal distributions with an accumulation of density mass at the limits, and a gap between 100% quality of life and the next smaller utility value. The package 'aldvmm' uses the likelihood and expected value functions proposed by Hernandez Alava and Wailoo (2015) <doi:10.1177/1536867X1501500307> using normal component distributions and a multinomial logit model of probabilities of component membership.

Maintained by Mark Pletscher. Last updated 1 years ago.

clinical-trials cost-effectiveness eq5d finite-mixture health-economics hta hui limited-dependent-variable mapping mixture-model patient-reported-outcomes quality-of-life utilities

7.5 match 5 stars 4.40 score 2 scripts

hojsgaard

geepack:Generalized Estimating Equation Package

Generalized estimating equations solver for parameters in mean, scale, and correlation structures, through mean link, scale link, and correlation link. Can also handle clustered categorical responses. See e.g. Halekoh and Højsgaard, (2005, <doi:10.18637/jss.v015.i02>), for details.

Maintained by Søren Højsgaard. Last updated 7 months ago.

cpp

3.4 match 1 stars 9.59 score 1.7k scripts 43 dependents

ropensci

aorsf:Accelerated Oblique Random Forests

Fit, interpret, and compute predictions with oblique random forests. Includes support for partial dependence, variable importance, passing customized functions for variable importance and identification of linear combinations of features. Methods for the oblique random survival forest are described in Jaeger et al., (2023) <DOI:10.1080/10618600.2023.2231048>.

Maintained by Byron Jaeger. Last updated 3 days ago.

data-science oblique random-forest survival openblas cpp openmp

3.5 match 58 stars 9.21 score 60 scripts 1 dependents

optad

adoptr:Adaptive Optimal Two-Stage Designs

Optimize one or two-arm, two-stage designs for clinical trials with respect to several implemented objective criteria or custom objectives. Optimization under uncertainty and conditional (given stage-one outcome) constraints are supported. See Pilz et al. (2019) <doi:10.1002/sim.8291> and Kunzmann et al. (2021) <doi:10.18637/jss.v098.i09> for details.

Maintained by Maximilian Pilz. Last updated 5 months ago.

4.5 match 1 stars 7.09 score 39 scripts 1 dependents

wuqian77

TrialSize:R Functions for Chapter 3,4,6,7,9,10,11,12,14,15 of Sample Size Calculation in Clinical Research

Functions and Examples in Sample Size Calculation in Clinical Research.

Maintained by Vicky Qian Wu. Last updated 4 months ago.

cpp

8.4 match 3 stars 3.78 score 95 scripts 1 dependents

bioc

psichomics:Graphical Interface for Alternative Splicing Quantification, Analysis and Visualisation

Interactive R package with an intuitive Shiny-based graphical interface for alternative splicing quantification and integrative analyses of alternative splicing and gene expression based on The Cancer Genome Atlas (TCGA), the Genotype-Tissue Expression project (GTEx), Sequence Read Archive (SRA) and user-provided data. The tool interactively performs survival, dimensionality reduction and median- and variance-based differential splicing and gene expression analyses that benefit from the incorporation of clinical and molecular sample-associated features (such as tumour stage or survival). Interactive visual access to genomic mapping and functional annotation of selected alternative splicing events is also included.

Maintained by Nuno Saraiva-Agostinho. Last updated 5 months ago.

sequencing rnaseq alternativesplicing differentialsplicing transcription gui principalcomponent survival biomedicalinformatics transcriptomics immunooncology visualization multiplecomparison geneexpression differentialexpression alternative-splicing bioconductor data-analyses differential-gene-expression differential-splicing-analysis gene-expression gtex recount2 rna-seq-data splicing-quantification sra tcga vast-tools cpp

4.5 match 36 stars 6.95 score 31 scripts

cran

flexmix:Flexible Mixture Modeling

A general framework for finite mixtures of regression models using the EM algorithm is implemented. The E-step and all data handling are provided, while the M-step can be supplied by the user to easily define new models. Existing drivers implement mixtures of standard linear models, generalized linear models and model-based clustering.

Maintained by Bettina Gruen. Last updated 16 days ago.

3.8 match 5 stars 8.19 score 113 dependents

cran

mixAK:Multivariate Normal Mixture Models and Mixtures of Generalized Linear Mixed Models Including Model Based Clustering

Contains a mixture of statistical methods including the MCMC methods to analyze normal mixtures. Additionally, model based clustering methods are implemented to perform classification based on (multivariate) longitudinal (or otherwise correlated) data. The basis for such clustering is a mixture of multivariate generalized linear mixed models. The package is primarily related to the publications Komárek (2009, Comp. Stat. and Data Anal.) <doi:10.1016/j.csda.2009.05.006> and Komárek and Komárková (2014, J. of Stat. Soft.) <doi:10.18637/jss.v059.i12>. It also implements methods published in Komárek and Komárková (2013, Ann. of Appl. Stat.) <doi:10.1214/12-AOAS580>, Hughes, Komárek, Bonnett, Czanner, García-Fiñana (2017, Stat. in Med.) <doi:10.1002/sim.7397>, Jaspers, Komárek, Aerts (2018, Biom. J.) <doi:10.1002/bimj.201600253> and Hughes, Komárek, Czanner, García-Fiñana (2018, Stat. Meth. in Med. Res) <doi:10.1177/0962280216674496>.

Maintained by Arnošt Komárek. Last updated 6 months ago.

openblas

6.8 match 4 stars 4.54 score 108 scripts 3 dependents

taylor-arnold

ctrialsgov:Query Data from U.S. National Library of Medicine's Clinical Trials Database

Tools to create and query database from the U.S. National Library of Medicine's Clinical Trials database <https://clinicaltrials.gov/>. Functions provide access a variety of techniques for searching the data using range queries, categorical filtering, and by searching for full-text keywords. Minimal graphical tools are also provided for interactively exploring the constructed data.

Maintained by Taylor Arnold. Last updated 3 years ago.

8.8 match 3.46 score 29 scripts

hojsgaard

gRain:Bayesian Networks

Probability propagation in Bayesian networks, also known as graphical independence networks. Documentation of the package is provided in vignettes included in the package and in the paper by Højsgaard (2012, <doi:10.18637/jss.v046.i10>). See 'citation("gRain")' for details.

Maintained by Søren Højsgaard. Last updated 5 months ago.

cpp

3.3 match 2 stars 9.13 score 408 scripts 8 dependents

cran

randomizeR:Randomization for Clinical Trials

This tool enables the user to choose a randomization procedure based on sound scientific criteria. It comprises the generation of randomization sequences as well the assessment of randomization procedures based on carefully selected criteria. Furthermore, 'randomizeR' provides a function for the comparison of randomization procedures.

Maintained by Ralf-Dieter Hilgers. Last updated 1 years ago.

9.0 match 2 stars 3.38 score 1 dependents

hputter

dynpred:Companion Package to "Dynamic Prediction in Clinical Survival Analysis"

The dynpred package contains functions for dynamic prediction in survival analysis.

Maintained by Hein Putter. Last updated 10 years ago.

9.3 match 2 stars 3.23 score 40 scripts 1 dependents

bioc

terraTCGAdata:OpenAccess TCGA Data on Terra as MultiAssayExperiment

Leverage the existing open access TCGA data on Terra with well-established Bioconductor infrastructure. Make use of the Terra data model without learning its complexities. With a few functions, you can copy / download and generate a MultiAssayExperiment from the TCGA example workspaces provided by Terra.

Maintained by Marcel Ramos. Last updated 5 months ago.

software infrastructure dataimport bioconductor-package

6.5 match 4.60 score 4 scripts

cran

Umpire:Simulating Realistic Gene Expression and Clinical Data

The Ultimate Microrray Prediction, Reality and Inference Engine (UMPIRE) is a package to facilitate the simulation of realistic microarray data sets with links to associated outcomes. See Zhang and Coombes (2012) <doi:10.1186/1471-2105-13-S13-S1>. Version 2.0 adds the ability to simulate realistic mixed-typed clinical data.

Maintained by Kevin R. Coombes. Last updated 1 months ago.

10.7 match 2.78 score

abdel-elsayed87

GRIN2:Genomic Random Interval (GRIN)

Improved version of 'GRIN' software that streamlines its use in practice to analyze genomic lesion data, accelerate its computing, and expand its analysis capabilities to answer additional scientific questions including a rigorous evaluation of the association of genomic lesions with RNA expression. Pounds, Stan, et al. (2013) <DOI:10.1093/bioinformatics/btt372>.

Maintained by Abdelrahman Elsayed. Last updated 4 months ago.

8.9 match 3.30 score

sidiwang

snSMART:Small N Sequential Multiple Assignment Randomized Trial Methods

Consolidated data simulation, sample size calculation and analysis functions for several snSMART (small sample sequential, multiple assignment, randomized trial) designs under one library. See Wei, B., Braun, T.M., Tamura, R.N. and Kidwell, K.M. "A Bayesian analysis of small n sequential multiple assignment randomized trials (snSMARTs)." (2018) Statistics in medicine, 37(26), pp.3723-3732 <doi:10.1002/sim.7900>.

Maintained by Michael Kleinsasser. Last updated 5 months ago.

bayesian-analysis clinical-trials rare-disease small-sample jags cpp

7.5 match 1 stars 3.90 score 3 scripts

ohdsi

CohortConstructor:Build and Manipulate Study Cohorts Using a Common Data Model

Create and manipulate study cohorts in data mapped to the Observational Medical Outcomes Partnership Common Data Model.

Maintained by Edward Burn. Last updated 3 days ago.

3.0 match 2 stars 9.71 score 207 scripts 2 dependents

darwin-eu

CodelistGenerator:Identify Relevant Clinical Codes and Evaluate Their Use

Generate a candidate code list for the Observational Medical Outcomes Partnership (OMOP) common data model based on string matching. For a given search strategy, a candidate code list will be returned.

Maintained by Edward Burn. Last updated 25 days ago.

2.9 match 13 stars 9.87 score 165 scripts 4 dependents

cran

RPointCloud:Visualizing Topological Loops and Voids

Visualizations to explain the results of a topological data analysis. The goal of topological data analysis is to identify persistent topological structures, such as loops (topological circles) and voids (topological spheres), in data sets. The output of an analysis using the 'TDA' package is a Rips diagram (named after the mathematician Eliyahu Rips). The goal of 'RPointCloud' is to fill in these holes in the data by providing tools to visualize the features that help explain the structures found in the Rips diagram. See McGee and colleagues (2024) <doi:10.1101/2024.05.16.593927>.

Maintained by Kevin R. Coombes. Last updated 7 months ago.

10.3 match 2.78 score

nbarrowman

vtree:Display Information About Nested Subsets of a Data Frame

A tool for calculating and drawing "variable trees". Variable trees display information about nested subsets of a data frame.

Maintained by Nick Barrowman. Last updated 13 hours ago.

data-science data-visualization exploratory-data-analysis statistics

4.0 match 76 stars 7.09 score 65 scripts

safetygraphics

safetyGraphics:Interactive Graphics for Monitoring Clinical Trial Safety

A framework for evaluation of clinical trial safety. Users can interactively explore their data using the included 'Shiny' application.

Maintained by Jeremy Wildfire. Last updated 2 years ago.

3.5 match 98 stars 8.18 score 111 scripts

bioc

RTCGA:The Cancer Genome Atlas Data Integration

The Cancer Genome Atlas (TCGA) Data Portal provides a platform for researchers to search, download, and analyze data sets generated by TCGA. It contains clinical information, genomic characterization data, and high level sequence analysis of the tumor genomes. The key is to understand genomics to improve cancer care. RTCGA package offers download and integration of the variety and volume of TCGA data using patient barcode key, what enables easier data possession. This may have an benefcial infuence on impact on development of science and improvement of patients' treatment. Furthermore, RTCGA package transforms TCGA data to tidy form which is convenient to use.

Maintained by Marcin Kosinski. Last updated 5 months ago.

immunooncology software dataimport datarepresentation preprocessing rnaseq survival dnamethylation principalcomponent visualization

3.2 match 51 stars 8.91 score 106 scripts 1 dependents

dongwenluo

predictmeans:Predicted Means for Linear and Semiparametric Models

Providing functions to diagnose and make inferences from various linear models, such as those obtained from 'aov', 'lm', 'glm', 'gls', 'lme', 'lmer', 'glmmTMB' and 'semireg'. Inferences include predicted means and standard errors, contrasts, multiple comparisons, permutation tests, adjusted R-square and graphs.

Maintained by Dongwen Luo. Last updated 11 months ago.

4.5 match 2 stars 6.26 score 152 scripts 2 dependents

mlr-org

mlr3proba:Probabilistic Supervised Learning for 'mlr3'

Provides extensions for probabilistic supervised learning for 'mlr3'. This includes extending the regression task to probabilistic and interval regression, adding a survival task, and other specialized models, predictions, and measures.

Maintained by John Zobolas. Last updated 2 months ago.

density-estimation machine-learning mlr3 probabilistic-regression probabilistic-supervised-learning supervised-learning survival-analysis cpp

3.6 match 135 stars 7.78 score 246 scripts

lilleoel

clintools:Tools for Clinical Research

Every research team have their own script for data management, statistics and most importantly hemodynamic indices. The purpose is to standardize scripts utilized in clinical research. The hemodynamic indices can be used in a long-format dataframe, and add both periods of interest (trigger-periods), and delete artifacts with deleter-files. Transfer function analysis (Claassen et al. (2016) <doi:10.1177/0271678X15626425>) and Mx (Czosnyka et al. (1996) <doi:10.1161/01.str.27.10.1829>) can be calculated using this package.

Maintained by Markus Harboe Olsen. Last updated 14 days ago.

7.4 match 2 stars 3.78 score

yayayaoyaoyao

RARtrials:Response-Adaptive Randomization in Clinical Trials

Some response-adaptive randomization methods commonly found in literature are included in this package. These methods include the randomized play-the-winner rule for binary endpoint (Wei and Durham (1978) <doi:10.2307/2286290>), the doubly adaptive biased coin design with minimal variance strategy for binary endpoint (Atkinson and Biswas (2013) <doi:10.1201/b16101>, Rosenberger and Lachin (2015) <doi:10.1002/9781118742112>) and maximal power strategy targeting Neyman allocation for binary endpoint (Tymofyeyev, Rosenberger, and Hu (2007) <doi:10.1198/016214506000000906>) and RSIHR allocation with each letter representing the first character of the names of the individuals who first proposed this rule (Youngsook and Hu (2010) <doi:10.1198/sbr.2009.0056>, Bello and Sabo (2016) <doi:10.1080/00949655.2015.1114116>), A-optimal Allocation for continuous endpoint (Sverdlov and Rosenberger (2013) <doi:10.1080/15598608.2013.783726>), Aa-optimal Allocation for continuous endpoint (Sverdlov and Rosenberger (2013) <doi:10.1080/15598608.2013.783726>), generalized RSIHR allocation for continuous endpoint (Atkinson and Biswas (2013) <doi:10.1201/b16101>), Bayesian response-adaptive randomization with a control group using the Thall \& Wathen method for binary and continuous endpoints (Thall and Wathen (2007) <doi:10.1016/j.ejca.2007.01.006>) and the forward-looking Gittins index rule for binary and continuous endpoints (Villar, Wason, and Bowden (2015) <doi:10.1111/biom.12337>, Williamson and Villar (2019) <doi:10.1111/biom.13119>).

Maintained by Chuyao Xu. Last updated 2 months ago.

6.0 match 4.65 score

bioc

randPack:Randomization routines for Clinical Trials

A suite of classes and functions for randomizing patients in clinical trials.

Maintained by Robert Gentleman. Last updated 5 months ago.

statisticalmethod

8.4 match 3.30 score 5 scripts

rsparapa

BART:Bayesian Additive Regression Trees

Bayesian Additive Regression Trees (BART) provide flexible nonparametric modeling of covariates for continuous, binary, categorical and time-to-event outcomes. For more information see Sparapani, Spanbauer and McCulloch <doi:10.18637/jss.v097.i01>.

Maintained by Rodney Sparapani. Last updated 9 months ago.

cpp openmp

3.5 match 14 stars 7.96 score 474 scripts 10 dependents

el-meyer

CohortPlat:Simulation of Cohort Platform Trials for Combination Treatments

A collection of functions dedicated to simulating staggered entry platform trials whereby the treatment under investigation is a combination of two active compounds. In order to obtain approval for this combination therapy, superiority of the combination over the two active compounds and superiority of the two active compounds over placebo need to be demonstrated. A more detailed description of the design can be found in Meyer et al. <DOI:10.1002/pst.2194> and a manual in Meyer et al. <arXiv:2202.02182>.

Maintained by Elias Laurin Meyer. Last updated 1 years ago.

clinical-trials platform-trials simulations

7.5 match 3.70 score 3 scripts

fanglu-chen

CARM:Covariate-Adjusted Adaptive Randomization via Mahalanobis-Distance

In randomized controlled trial (RCT), balancing covariate is often one of the most important concern. CARM package provides functions to balance the covariates and generate allocation sequence by covariate-adjusted Adaptive Randomization via Mahalanobis-distance (ARM) for RCT. About what ARM is and how it works please see Y. Qin, Y. Li, W. Ma, H. Yang, and F. Hu (2022). "Adaptive randomization via Mahalanobis distance" Statistica Sinica. <doi:10.5705/ss.202020.0440>. In addition, the package is also suitable for the randomization process of multi-arm trials. For details, please see Yang H, Qin Y, Wang F, et al. (2023). "Balancing covariates in multi-arm trials via adaptive randomization" Computational Statistics & Data Analysis.<doi:10.1016/j.csda.2022.107642>.

Maintained by Fanglu Chen. Last updated 8 months ago.

adaptive-design clinical-trials

7.5 match 5 stars 3.70 score

patriciamar

ShinyItemAnalysis:Test and Item Analysis via Shiny

Package including functions and interactive shiny application for the psychometric analysis of educational tests, psychological assessments, health-related and other types of multi-item measurements, or ratings from multiple raters.

Maintained by Patricia Martinkova. Last updated 1 months ago.

assessment differential-item-functioning item-analysis item-response-theory psychometrics shiny

3.5 match 44 stars 7.88 score 105 scripts 3 dependents

jinghuazhao

pan:Multiple Imputation for Multivariate Panel or Clustered Data

It provides functions and examples for maximum likelihood estimation for generalized linear mixed models and Gibbs sampler for multivariate linear mixed models with incomplete data, as described in Schafer JL (1997) "Imputation of missing covariates under a multivariate linear mixed model". Technical report 97-04, Dept. of Statistics, The Pennsylvania State University.

Maintained by Jing hua Zhao. Last updated 2 years ago.

fortran

3.5 match 1 stars 7.86 score 65 scripts 155 dependents

adayim

consort:Create Consort Diagram

To make it easy to create CONSORT diagrams for the transparent reporting of participant allocation in randomized, controlled clinical trials. This is done by creating a standardized disposition data, and using this data as the source for the creation a standard CONSORT diagram. Human effort by supplying text labels on the node can also be achieved.

Maintained by Alim Dayim. Last updated 1 months ago.

4.1 match 32 stars 6.62 score 59 scripts

lilyclements

carbonr:Calculate Carbon-Equivalent Emissions

Provides a flexible tool for calculating carbon-equivalent emissions. Mostly using data from the UK Government's Greenhouse Gas Conversion Factors report <https://www.gov.uk/government/publications/greenhouse-gas-reporting-conversion-factors-2023>, it facilitates transparent emissions calculations for various sectors, including travel, accommodation, and clinical activities. The package is designed for easy integration into R workflows, with additional support for 'shiny' applications and community-driven extensions.

Maintained by Lily Clements. Last updated 5 months ago.

8.6 match 3.16 score 29 scripts

thothorn

exactRankTests:Exact Distributions for Rank and Permutation Tests

Computes exact conditional p-values and quantiles using an implementation of the Shift-Algorithm by Streitberg & Roehmel.

Maintained by Torsten Hothorn. Last updated 3 years ago.

3.8 match 1 stars 7.13 score 276 scripts 65 dependents

genentech

psborrow2:Bayesian Dynamic Borrowing Analysis and Simulation

Bayesian dynamic borrowing is an approach to incorporating external data to supplement a randomized, controlled trial analysis in which external data are incorporated in a dynamic way (e.g., based on similarity of outcomes); see Viele 2013 <doi:10.1002/pst.1589> for an overview. This package implements the hierarchical commensurate prior approach to dynamic borrowing as described in Hobbes 2011 <doi:10.1111/j.1541-0420.2011.01564.x>. There are three main functionalities. First, 'psborrow2' provides a user-friendly interface for applying dynamic borrowing on the study results handles the Markov Chain Monte Carlo sampling on behalf of the user. Second, 'psborrow2' provides a simulation framework to compare different borrowing parameters (e.g. full borrowing, no borrowing, dynamic borrowing) and other trial and borrowing characteristics (e.g. sample size, covariates) in a unified way. Third, 'psborrow2' provides a set of functions to generate data for simulation studies, and also allows the user to specify their own data generation process. This package is designed to use the sampling functions from 'cmdstanr' which can be installed from <https://stan-dev.r-universe.dev>.

Maintained by Matt Secrest. Last updated 1 months ago.

bayesian-dynamic-borrowing psborrow2 simulation-study

3.4 match 18 stars 7.87 score 16 scripts

mrc-ide

malariasimulation:An individual based model for malaria

Specifies the latest and greatest malaria model.

Maintained by Giovanni Charles. Last updated 27 days ago.

cpp

3.3 match 16 stars 8.17 score 146 scripts

bioc

IMAS:Integrative analysis of Multi-omics data for Alternative Splicing

Integrative analysis of Multi-omics data for Alternative splicing.

Maintained by Seonggyun Han. Last updated 5 months ago.

immunooncology alternativesplicing differentialexpression differentialsplicing geneexpression generegulation regression rnaseq sequencing snp software transcription

5.5 match 4.85 score 1 scripts

atorus-research

metacore:A Centralized Metadata Object Focus on Clinical Trial Data Programming Workflows

Create an immutable container holding metadata for the purpose of better enabling programming activities and functionality of other packages within the clinical programming workflow.

Maintained by Christina Fillmore. Last updated 11 months ago.

3.3 match 35 stars 7.99 score 133 scripts 1 dependents

jackmwolf

tehtuner:Fit and Tune Models to Detect Treatment Effect Heterogeneity

Implements methods to fit Virtual Twins models (Foster et al. (2011) <doi:10.1002/sim.4322>) for identifying subgroups with differential effects in the context of clinical trials while controlling the probability of falsely detecting a differential effect when the conditional average treatment effect is uniform across the study population using parameter selection methods proposed in Wolf et al. (2022) <doi:10.1177/17407745221095855>.

Maintained by Jack Wolf. Last updated 2 years ago.

clinical-trials heterogeneity-of-treatment-effect subgroup-identification

8.0 match 4 stars 3.30 score 6 scripts

huanglabumn

oncoPredict:Drug Response Modeling and Biomarker Discovery

Allows for building drug response models using screening data between bulk RNA-Seq and a drug response metric and two additional tools for biomarker discovery that have been developed by the Huang Laboratory at University of Minnesota. There are 3 main functions within this package. (1) calcPhenotype is used to build drug response models on RNA-Seq data and impute them on any other RNA-Seq dataset given to the model. (2) GLDS is used to calculate the general level of drug sensitivity, which can improve biomarker discovery. (3) IDWAS can take the results from calcPhenotype and link the imputed response back to available genomic (mutation and CNV alterations) to identify biomarkers. Each of these functions comes from a paper from the Huang research laboratory. Below gives the relevant paper for each function. calcPhenotype - Geeleher et al, Clinical drug response can be predicted using baseline gene expression levels and in vitro drug sensitivity in cell lines. GLDS - Geeleher et al, Cancer biomarker discovery is improved by accounting for variability in general levels of drug sensitivity in pre-clinical models. IDWAS - Geeleher et al, Discovering novel pharmacogenomic biomarkers by imputing drug response in cancer patients from large genomics studies.

Maintained by Robert Gruener. Last updated 12 months ago.

sva preprocesscore stringr biomart genefilter org.hs.eg.db genomicfeatures txdb.hsapiens.ucsc.hg19.knowngene tcgabiolinks biocgenerics genomicranges iranges s4vectors

4.0 match 18 stars 6.47 score 41 scripts

pharmaverse

logrx:A Logging Utility Focus on Clinical Trial Programming Workflows

A utility to facilitate the logging and review of R programs in clinical trial programming workflows.

Maintained by Nathan Kosiba. Last updated 10 days ago.

3.4 match 41 stars 7.60 score 15 scripts

drizopoulos

JM:Joint Modeling of Longitudinal and Survival Data

Shared parameter models for the joint modeling of longitudinal and time-to-event data.

Maintained by Dimitris Rizopoulos. Last updated 3 years ago.

5.3 match 2 stars 4.93 score 112 scripts 1 dependents

glsnow

blockrand:Randomization for Block Random Clinical Trials

Create randomizations for block random clinical trials. Can also produce a pdf file of randomization cards.

Maintained by Greg Snow. Last updated 5 years ago.

7.1 match 2 stars 3.60 score 67 scripts 1 dependents

boehringer-ingelheim

BPrinStratTTE:Causal Effects in Principal Strata Defined by Antidrug Antibodies

Bayesian models to estimate causal effects of biological treatments on time-to-event endpoints in clinical trials with principal strata defined by the occurrence of antidrug antibodies. The methodology is based on Frangakis and Rubin (2002) <doi:10.1111/j.0006-341x.2002.00021.x> and Imbens and Rubin (1997) <doi:10.1214/aos/1034276631>, and here adapted to a specific time-to-event setting.

Maintained by Christian Stock. Last updated 11 months ago.

bayesian-methods causal-inference clinical-trial estimand mcmc-methods pharmaceutical-development principal-stratification simulation stan time-to-event cpp

8.0 match 3.18 score

bruneiverse

bruneimap:Maps and Spatial Data of Brunei

Provides spatial data for mapping Brunei, including boundaries for districts, mukims, and kampongs, as well as locations of key infrastructure such as masjids, hospitals, clinics, and schools. The package supports researchers, analysts, and developers working with Brunei’s geographic and demographic data, offering a quick and accessible foundation for creating maps and conducting spatial studies.

Maintained by Haziq Jamil. Last updated 29 days ago.

brunei census maps

4.1 match 1 stars 6.10 score 25 scripts

tpatni719

gsMAMS:Group Sequential Designs of Multi-Arm Multi-Stage Trials

It provides functions to generate operating characteristics and to calculate Sequential Conditional Probability Ratio Tests(SCPRT) efficacy and futility boundary values along with sample/event size of Multi-Arm Multi-Stage(MAMS) trials for different outcomes. The package is based on Jianrong Wu, Yimei Li, Liang Zhu (2023) <doi:10.1002/sim.9682>, Jianrong Wu, Yimei Li (2023) "Group Sequential Multi-Arm Multi-Stage Survival Trial Design with Treatment Selection"(Manuscript accepted for publication) and Jianrong Wu, Yimei Li, Shengping Yang (2023) "Group Sequential Multi-Arm Multi-Stage Trial Design with Ordinal Endpoints"(In preparation).

Maintained by Tushar Patni. Last updated 10 months ago.

5.4 match 4.60 score 8 scripts

bnaras

ASSISTant:Adaptive Subgroup Selection in Group Sequential Trials

Clinical trial design for subgroup selection in three-stage group sequential trial. Includes facilities for design, exploration and analysis of such trials. An implementation of the initial DEFUSE-3 trial is also provided as a vignette.

Maintained by Balasubramanian Narasimhan. Last updated 5 years ago.

5.4 match 4.54 score 23 scripts

idem-lab

conmat:Builds Contact Matrices using GAMs and Population Data

Builds contact matrices using GAMs and population data. This package incorporates data that is copyright Commonwealth of Australia (Australian Electoral Commission and Australian Bureau of Statistics) 2020.

Maintained by Nicholas Tierney. Last updated 6 days ago.

contact-matrices infectious-diseases population-data public-health

3.4 match 19 stars 7.21 score 47 scripts

bioc

M3C:Monte Carlo Reference-based Consensus Clustering

M3C is a consensus clustering algorithm that uses a Monte Carlo simulation to eliminate overestimation of K and can reject the null hypothesis K=1.

Maintained by Christopher John. Last updated 5 months ago.

clustering geneexpression transcription rnaseq sequencing immunooncology

3.8 match 6.59 score 174 scripts 1 dependents

tkcaccia

KODAMA:Knowledge Discovery by Accuracy Maximization

An unsupervised and semi-supervised learning algorithm that performs feature extraction from noisy and high-dimensional data. It facilitates identification of patterns representing underlying groups on all samples in a data set. Based on Cacciatore S, Tenori L, Luchinat C, Bennett PR, MacIntyre DA. (2017) Bioinformatics <doi:10.1093/bioinformatics/btw705> and Cacciatore S, Luchinat C, Tenori L. (2014) Proc Natl Acad Sci USA <doi:10.1073/pnas.1220873111>.

Maintained by Stefano Cacciatore. Last updated 3 months ago.

openblas cpp

3.5 match 1 stars 6.98 score 63 scripts 1 dependents

bioc

TRONCO:TRONCO, an R package for TRanslational ONCOlogy

The TRONCO (TRanslational ONCOlogy) R package collects algorithms to infer progression models via the approach of Suppes-Bayes Causal Network, both from an ensemble of tumors (cross-sectional samples) and within an individual patient (multi-region or single-cell samples). The package provides parallel implementation of algorithms that process binary matrices where each row represents a tumor sample and each column a single-nucleotide or a structural variant driving the progression; a 0/1 value models the absence/presence of that alteration in the sample. The tool can import data from plain, MAF or GISTIC format files, and can fetch it from the cBioPortal for cancer genomics. Functions for data manipulation and visualization are provided, as well as functions to import/export such data to other bioinformatics tools for, e.g, clustering or detection of mutually exclusive alterations. Inferred models can be visualized and tested for their confidence via bootstrap and cross-validation. TRONCO is used for the implementation of the Pipeline for Cancer Inference (PICNIC).

Maintained by Luca De Sano. Last updated 5 months ago.

biomedicalinformatics bayesian graphandnetwork somaticmutation networkinference network clustering dataimport singlecell immunooncology algorithms cancer-inference tumors

3.8 match 30 stars 6.50 score 38 scripts

abusjahn

wrappedtools:Useful Wrappers Around Commonly Used Functions

The main functionalities of 'wrappedtools' are: adding backticks to variable names; rounding to desired precision with special case for p-values; selecting columns based on pattern and storing their position, name, and backticked name; computing and formatting of descriptive statistics (e.g. mean±SD), comparing groups and creating publication-ready tables with descriptive statistics and p-values; creating specialized plots for correlation matrices. Functions were mainly written for my own daily work or teaching, but may be of use to others as well.

Maintained by Andreas Busjahn. Last updated 5 months ago.

descriptive-statistics test-statistic

5.2 match 2 stars 4.70 score 8 scripts

bioc

ELMER:Inferring Regulatory Element Landscapes and Transcription Factor Networks Using Cancer Methylomes

ELMER is designed to use DNA methylation and gene expression from a large number of samples to infere regulatory element landscape and transcription factor network in primary tissue.

Maintained by Tiago Chedraoui Silva. Last updated 5 months ago.

dnamethylation geneexpression motifannotation software generegulation transcription network

3.3 match 7.42 score 176 scripts

bioc

tidytof:Analyze High-dimensional Cytometry Data Using Tidy Data Principles

This package implements an interactive, scientific analysis pipeline for high-dimensional cytometry data built using tidy data principles. It is specifically designed to play well with both the tidyverse and Bioconductor software ecosystems, with functionality for reading/writing data files, data cleaning, preprocessing, clustering, visualization, modeling, and other quality-of-life functions. tidytof implements a "grammar" of high-dimensional cytometry data analysis.

Maintained by Timothy Keyes. Last updated 5 months ago.

singlecell flowcytometry bioinformatics cytometry data-science single-cell tidyverse cpp

3.3 match 19 stars 7.26 score 35 scripts

bioc

MLInterfaces:Uniform interfaces to R machine learning procedures for data in Bioconductor containers

This package provides uniform interfaces to machine learning code for data in R and Bioconductor containers.

Maintained by Vincent Carey. Last updated 5 months ago.

classification clustering

3.2 match 7.63 score 79 scripts 6 dependents

bioc

PDATK:Pancreatic Ductal Adenocarcinoma Tool-Kit

Pancreatic ductal adenocarcinoma (PDA) has a relatively poor prognosis and is one of the most lethal cancers. Molecular classification of gene expression profiles holds the potential to identify meaningful subtypes which can inform therapeutic strategy in the clinical setting. The Pancreatic Cancer Adenocarcinoma Tool-Kit (PDATK) provides an S4 class-based interface for performing unsupervised subtype discovery, cross-cohort meta-clustering, gene-expression-based classification, and subsequent survival analysis to identify prognostically useful subtypes in pancreatic cancer and beyond. Two novel methods, Consensus Subtypes in Pancreatic Cancer (CSPC) and Pancreatic Cancer Overall Survival Predictor (PCOSP) are included for consensus-based meta-clustering and overall-survival prediction, respectively. Additionally, four published subtype classifiers and three published prognostic gene signatures are included to allow users to easily recreate published results, apply existing classifiers to new data, and benchmark the relative performance of new methods. The use of existing Bioconductor classes as input to all PDATK classes and methods enables integration with existing Bioconductor datasets, including the 21 pancreatic cancer patient cohorts available in the MetaGxPancreas data package. PDATK has been used to replicate results from Sandhu et al (2019) [https://doi.org/10.1200/cci.18.00102] and an additional paper is in the works using CSPC to validate subtypes from the included published classifiers, both of which use the data available in MetaGxPancreas. The inclusion of subtype centroids and prognostic gene signatures from these and other publications will enable researchers and clinicians to classify novel patient gene expression data, allowing the direct clinical application of the classifiers included in PDATK. Overall, PDATK provides a rich set of tools to identify and validate useful prognostic and molecular subtypes based on gene-expression data, benchmark new classifiers against existing ones, and apply discovered classifiers on novel patient data to inform clinical decision making.

Maintained by Benjamin Haibe-Kains. Last updated 5 months ago.

geneexpression pharmacogenetics pharmacogenomics software classification survival clustering geneprediction

5.6 match 1 stars 4.31 score 17 scripts

cu-dbmi-peds

phoenix:The Phoenix Pediatric Sepsis and Septic Shock Criteria

Implementation of the Phoenix and Phoenix-8 Sepsis Criteria as described in "Development and Validation of the Phoenix Criteria for Pediatric Sepsis and Septic Shock" by Sanchez-Pinto, Bennett, DeWitt, Russell et al. (2024) <doi:10.1001/jama.2024.0196> (Drs. Sanchez-Pinto and Bennett contributed equally to this manuscript; Dr. DeWitt and Mr. Russell contributed equally to the manuscript), "International Consensus Criteria for Pediatric Sepsis and Septic Shock" by Schlapbach, Watson, Sorce, Argent, et al. (2024) <doi:10.1001/jama.2024.0179> (Drs Schlapbach, Watson, Sorce, and Argent contributed equally) and the application note "phoenix: an R package and Python module for calculating the Phoenix pediatric sepsis score and criteria" by DeWitt, Russell, Rebull, Sanchez-Pinto, and Bennett (2024) <doi:10.1093/jamiaopen/ooae066>.

Maintained by Peter DeWitt. Last updated 13 days ago.

pediatric phoenix python sepsis septic-shock sql

4.2 match 3 stars 5.78 score 20 scripts

tirgit

missCompare:Intuitive Missing Data Imputation Framework

Offers a convenient pipeline to test and compare various missing data imputation algorithms on simulated and real data. These include simpler methods, such as mean and median imputation and random replacement, but also include more sophisticated algorithms already implemented in popular R packages, such as 'mi', described by Su et al. (2011) <doi:10.18637/jss.v045.i02>; 'mice', described by van Buuren and Groothuis-Oudshoorn (2011) <doi:10.18637/jss.v045.i03>; 'missForest', described by Stekhoven and Buhlmann (2012) <doi:10.1093/bioinformatics/btr597>; 'missMDA', described by Josse and Husson (2016) <doi:10.18637/jss.v070.i01>; and 'pcaMethods', described by Stacklies et al. (2007) <doi:10.1093/bioinformatics/btm069>. The central assumption behind 'missCompare' is that structurally different datasets (e.g. larger datasets with a large number of correlated variables vs. smaller datasets with non correlated variables) will benefit differently from different missing data imputation algorithms. 'missCompare' takes measurements of your dataset and sets up a sandbox to try a curated list of standard and sophisticated missing data imputation algorithms and compares them assuming custom missingness patterns. 'missCompare' will also impute your real-life dataset for you after the selection of the best performing algorithm in the simulations. The package also provides various post-imputation diagnostics and visualizations to help you assess imputation performance.

Maintained by Tibor V. Varga. Last updated 4 years ago.

comparison comparison-benchmarks imputation imputation-algorithm imputation-methods imputations kolmogorov-smirnov missing missing-data missing-data-imputation missing-status-check missing-values missingness post-imputation-diagnostics rmse

4.0 match 39 stars 5.89 score 40 scripts

usaid-oha-si

gophr:Utility functions related to working with the MER Structured Dataset

This packages contains a number of functions for working with the PEPFAR MSD.

Maintained by Aaron Chafetz. Last updated 4 months ago.

3.8 match 1 stars 6.21 score 182 scripts 1 dependents

ohdsi

omock:Creation of Mock Observational Medical Outcomes Partnership Common Data Model

Creates mock data for testing and package development for the Observational Medical Outcomes Partnership common data model. The package offers functions crafted with pipeline-friendly implementation, enabling users to effortlessly include only the necessary tables for their testing needs.

Maintained by Mike Du. Last updated 1 months ago.

3.1 match 2 stars 7.44 score 45 scripts 1 dependents

bioc

Maaslin2:"Multivariable Association Discovery in Population-scale Meta-omics Studies"

MaAsLin2 is comprehensive R package for efficiently determining multivariable association between clinical metadata and microbial meta'omic features. MaAsLin2 relies on general linear models to accommodate most modern epidemiological study designs, including cross-sectional and longitudinal, and offers a variety of data exploration, normalization, and transformation methods. MaAsLin2 is the next generation of MaAsLin.

Maintained by Lauren McIver. Last updated 5 months ago.

metagenomics software microbiome normalization biobakery bioconductor differential-abundance-analysis false-discovery-rate multiple-covariates public repeated-measures tools

2.1 match 133 stars 11.03 score 532 scripts 3 dependents

anestistouloumis

multgee:GEE Solver for Correlated Nominal or Ordinal Multinomial Responses

GEE solver for correlated nominal or ordinal multinomial responses using a local odds ratios parameterization.

Maintained by Anestis Touloumis. Last updated 12 months ago.

gee multinomial cpp

3.8 match 9 stars 6.16 score 120 scripts 1 dependents

cran

bst:Gradient Boosting

Functional gradient descent algorithm for a variety of convex and non-convex loss functions, for both classical and robust regression and classification problems. See Wang (2011) <doi:10.2202/1557-4679.1304>, Wang (2012) <doi:10.3414/ME11-02-0020>, Wang (2018) <doi:10.1080/10618600.2018.1424635>, Wang (2018) <doi:10.1214/18-EJS1404>.

Maintained by Zhu Wang. Last updated 2 years ago.

5.5 match 4.17 score 5 dependents

alecri

dosresmeta:Multivariate Dose-Response Meta-Analysis

Estimates dose-response relations from summarized dose-response data and to combines them according to principles of (multivariate) random-effects models.

Maintained by Alessio Crippa. Last updated 6 years ago.

3.5 match 11 stars 6.56 score 66 scripts

fridleylab

spatialTIME:Spatial Analysis of Vectra Immunoflourescent Data

Visualization and analysis of Vectra Immunoflourescent data. Options for calculating both the univariate and bivariate Ripley's K are included. Calculations are performed using a permutation-based approach presented by Wilson et al. <doi:10.1101/2021.04.27.21256104>.

Maintained by Fridley Lab. Last updated 7 months ago.

3.8 match 4 stars 6.08 score 30 scripts

cran

MLEcens:Computation of the MLE for Bivariate Interval Censored Data

We provide functions to compute the nonparametric maximum likelihood estimator (MLE) for the bivariate distribution of (X,Y), when realizations of (X,Y) cannot be observed directly. To be more precise, we consider the situation where we observe a set of rectangles in R^2 that are known to contain the unobservable realizations of (X,Y). We compute the MLE based on such a set of rectangles. The methods can also be used for univariate censored data (see data set 'cosmesis'), and for censored data with competing risks (see data set 'menopause'). We also provide functions to visualize the observed data and the MLE.

Maintained by Marloes Maathuis. Last updated 6 months ago.

openblas

6.7 match 3.38 score 20 scripts 17 dependents

johnschwenck

bp:Blood Pressure Analysis in R

A comprehensive package to aid in the analysis of blood pressure data of all forms by providing both descriptive and visualization tools for researchers.

Maintained by John Schwenck. Last updated 3 years ago.

3.6 match 26 stars 6.23 score 13 scripts

mjuraska

CoRpower:Power Calculations for Assessing Correlates of Risk in Clinical Efficacy Trials

Calculates power for assessment of intermediate biomarker responses as correlates of risk in the active treatment group in clinical efficacy trials, as described in Gilbert, Janes, and Huang, Power/Sample Size Calculations for Assessing Correlates of Risk in Clinical Efficacy Trials (2016, Statistics in Medicine). The methods differ from past approaches by accounting for the level of clinical treatment efficacy overall and in biomarker response subgroups, which enables the correlates of risk results to be interpreted in terms of potential correlates of efficacy/protection. The methods also account for inter-individual variability of the observed biomarker response that is not biologically relevant (e.g., due to technical measurement error of the laboratory assay used to measure the biomarker response), which is important because power to detect a specified correlate of risk effect size is heavily affected by the biomarker's measurement error. The methods can be used for a general binary clinical endpoint model with a univariate dichotomous, trichotomous, or continuous biomarker response measured in active treatment recipients at a fixed timepoint after randomization, with either case-cohort Bernoulli sampling or case-control without-replacement sampling of the biomarker (a baseline biomarker is handled as a trivial special case). In a specified two-group trial design, the computeN() function can initially be used for calculating additional requisite design parameters pertaining to the target population of active treatment recipients observed to be at risk at the biomarker sampling timepoint. Subsequently, the power calculation employs an inverse probability weighted logistic regression model fitted by the tps() function in the 'osDesign' package. Power results as well as the relationship between the correlate of risk effect size and treatment efficacy can be visualized using various plotting functions. To link power calculations for detecting a correlate of risk and a correlate of treatment efficacy, a baseline immunogenicity predictor (BIP) can be simulated according to a specified classification rule (for dichotomous or trichotomous BIPs) or correlation with the biomarker response (for continuous BIPs), then outputted along with biomarker response data under assignment to treatment, and clinical endpoint data for both treatment and placebo groups.

Maintained by Michal Juraska. Last updated 4 years ago.

5.4 match 4.15 score 14 scripts

davidchampredon

ern:Effective Reproduction Number Estimation

Estimate the effective reproduction number from wastewater and clinical data sources.

Maintained by David Champredon. Last updated 2 months ago.

jags cpp

9.1 match 2.45 score 14 scripts

cran

medExtractR:Extraction of Medication Information from Clinical Text

Function and support for medication and dosing information extraction from free-text clinical notes. Medication entities for the basic medExtractR implementation that can be extracted include drug name, strength, dose amount, dose, frequency, intake time, dose change, and time of last dose. The basic medExtractR is outlined in Weeks, Beck, McNeer, Williams, Bejan, Denny, Choi (2020) <doi: 10.1093/jamia/ocz207>. The extended medExtractR_tapering implementation is intended to extract dosing information for more tapering schedules, which are far more complex. The tapering extension allows for the extraction of additional entities including dispense amount, refills, dose schedule, time keyword, transition, and preposition.

Maintained by Leena Choi. Last updated 3 years ago.

7.0 match 3.18 score 9 scripts

insightsengineering

dunlin:Preprocessing Tools for Clinical Trial Data

A collection of functions to preprocess data and organize them in a format amenable to use by chevron.

Maintained by Joe Zhu. Last updated 24 days ago.

3.0 match 4 stars 7.38 score 30 scripts 1 dependents

astrazeneca

maraca:The Maraca Plot: Visualization of Hierarchical Composite Endpoints in Clinical Trials

Library that supports visual interpretation of hierarchical composite endpoints (HCEs). HCEs are complex constructs used as primary endpoints in clinical trials, combining outcomes of different types into ordinal endpoints, in which each patient contributes the most clinically important event (one and only one) to the analysis. See Karpefors M et al. (2022) <doi:10.1177/17407745221134949>.

Maintained by Monika Huhn. Last updated 4 months ago.

3.6 match 3 stars 6.11 score 16 scripts

bioc

GDCRNATools:GDCRNATools: an R/Bioconductor package for integrative analysis of lncRNA, mRNA, and miRNA data in GDC

This is an easy-to-use package for downloading, organizing, and integrative analyzing RNA expression data in GDC with an emphasis on deciphering the lncRNA-mRNA related ceRNA regulatory network in cancer. Three databases of lncRNA-miRNA interactions including spongeScan, starBase, and miRcode, as well as three databases of mRNA-miRNA interactions including miRTarBase, starBase, and miRcode are incorporated into the package for ceRNAs network construction. limma, edgeR, and DESeq2 can be used to identify differentially expressed genes/miRNAs. Functional enrichment analyses including GO, KEGG, and DO can be performed based on the clusterProfiler and DO packages. Both univariate CoxPH and KM survival analyses of multiple genes can be implemented in the package. Besides some routine visualization functions such as volcano plot, bar plot, and KM plot, a few simply shiny apps are developed to facilitate visualization of results on a local webpage.

Maintained by Ruidong Li. Last updated 5 months ago.

immunooncology geneexpression differentialexpression generegulation genetarget networkinference survival visualization genesetenrichment networkenrichment network rnaseq go kegg

3.9 match 5.64 score 44 scripts

smouksassi

coveffectsplot:Produce Forest Plots to Visualize Covariate Effects

Produce forest plots to visualize covariate effects using either the command line or an interactive 'Shiny' application.

Maintained by Samer Mouksassi. Last updated 1 months ago.

2.8 match 32 stars 7.86 score 40 scripts

massimoaria

pubmedR:Gathering Metadata About Publications, Grants, Clinical Trials from 'PubMed' Database

A set of tools to extract bibliographic content from 'PubMed' database using 'NCBI' REST API <https://www.ncbi.nlm.nih.gov/home/develop/api/>.

Maintained by Massimo Aria. Last updated 12 months ago.

2.8 match 38 stars 7.70 score 39 scripts 3 dependents

shannonpileggi

gtreg:Regulatory Tables for Clinical Research

Creates tables suitable for regulatory agency submission by leveraging the 'gtsummary' package as the back end. Tables can be exported to HTML, Word, PDF and more. Highly customized outputs are available by utilizing existing styling functions from 'gtsummary' as well as custom options designed for regulatory tables.

Maintained by Shannon Pileggi. Last updated 24 days ago.

3.1 match 37 stars 6.92 score 30 scripts

laylaparast

SurrogateSeq:Group Sequential Testing of a Treatment Effect Using a Surrogate Marker

Provides functions to implement group sequential procedures that allow for early stopping to declare efficacy using a surrogate marker and the possibility of futility stopping. More details will be available in the future in: Parast, L. and Bartroff, J (2024) "Group Sequential Testing of a Treatment Effect Using a Surrogate Marker".

Maintained by Layla Parast. Last updated 2 months ago.

7.2 match 3.00 score 1 scripts

bioc

CINdex:Chromosome Instability Index

The CINdex package addresses important area of high-throughput genomic analysis. It allows the automated processing and analysis of the experimental DNA copy number data generated by Affymetrix SNP 6.0 arrays or similar high throughput technologies. It calculates the chromosome instability (CIN) index that allows to quantitatively characterize genome-wide DNA copy number alterations as a measure of chromosomal instability. This package calculates not only overall genomic instability, but also instability in terms of copy number gains and losses separately at the chromosome and cytoband level.

Maintained by Yuriy Gusev. Last updated 5 months ago.

software copynumbervariation genomicvariation acgh microarray genetics sequencing

5.3 match 4.08 score 2 scripts

el-meyer

cats:Cohort Platform Trial Simulation

Cohort plAtform Trial Simulation whereby every cohort consists of two arms, control and experimental treatment. Endpoints are co-primary binary endpoints and decisions are made using either Bayesian or frequentist decision rules. Realistic trial trajectories are simulated and the operating characteristics of the designs are calculated.

Maintained by Elias Laurin Meyer. Last updated 1 years ago.

clinical-trials platform-trials simulations

7.5 match 2.85 score 14 scripts

bgcarlisle

cthist:Clinical Trial Registry History

Retrieves historical versions of clinical trial registry entries from <https://ClinicalTrials.gov>. Package functionality and implementation for v 1.0.0 is documented in Carlisle (2022) <DOI:10.1371/journal.pone.0270909>.

Maintained by Benjamin Gregory Carlisle. Last updated 8 months ago.

5.2 match 8 stars 4.08 score 6 scripts

bioc

metagenomeSeq:Statistical analysis for sparse high-throughput sequencing

metagenomeSeq is designed to determine features (be it Operational Taxanomic Unit (OTU), species, etc.) that are differentially abundant between two or more groups of multiple samples. metagenomeSeq is designed to address the effects of both normalization and under-sampling of microbial communities on disease association detection and the testing of feature correlations.

Maintained by Joseph N. Paulson. Last updated 3 months ago.

immunooncology classification clustering geneticvariability differentialexpression microbiome metagenomics normalization visualization multiplecomparison sequencing software

1.8 match 69 stars 12.02 score 494 scripts 7 dependents

spgarbet

tangram:The Grammar of Tables

Provides an extensible formula system to quickly and easily create production quality tables. The processing steps are a formula parser, statistical content generation from data as defined by formula, followed by rendering into a table. Each step of the processing is separate and user definable thus creating a set of composable building blocks for highly customizable table generation. A user is not limited by any of the choices of the package creator other than the formula grammar. For example, one could chose to add a different S3 rendering function and output a format not provided in the default package, or possibly one would rather have Gini coefficients for their statistical content in a resulting table. Routines to achieve New England Journal of Medicine style, Lancet style and Hmisc::summaryM() statistics are provided. The package contains rendering for HTML5, Rmarkdown and an indexing format for use in tracing and tracking are provided.

Maintained by Shawn Garbett. Last updated 2 years ago.

3.5 match 68 stars 5.93 score 62 scripts

bioc

ISAnalytics:Analyze gene therapy vector insertion sites data identified from genomics next generation sequencing reads for clonal tracking studies

In gene therapy, stem cells are modified using viral vectors to deliver the therapeutic transgene and replace functional properties since the genetic modification is stable and inherited in all cell progeny. The retrieval and mapping of the sequences flanking the virus-host DNA junctions allows the identification of insertion sites (IS), essential for monitoring the evolution of genetically modified cells in vivo. A comprehensive toolkit for the analysis of IS is required to foster clonal trackign studies and supporting the assessment of safety and long term efficacy in vivo. This package is aimed at (1) supporting automation of IS workflow, (2) performing base and advance analysis for IS tracking (clonal abundance, clonal expansions and statistics for insertional mutagenesis, etc.), (3) providing basic biology insights of transduced stem cells in vivo.

Maintained by Francesco Gazzo. Last updated 3 months ago.

biomedicalinformatics sequencing singlecell

3.5 match 3 stars 5.83 score 15 scripts

sinnweja

smartDesign:Sequential Multiple Assignment Randomized Trial Design

SMART trial design, as described by He, J., McClish, D., Sabo, R. (2021) <doi:10.1080/19466315.2021.1883472>, includes multiple stages of randomization, where participants are randomized to an initial treatment in the first stage and then subsequently re-randomized between treatments in the following stage.

Maintained by Jason Sinnwell. Last updated 1 years ago.

6.7 match 3.00 score 1 scripts

ccicb

CRUX:Easily explore patterns of somatic variation in cancer using 'CRUX'

Shiny app for exploring somatic variation in cancer. Powered by maftools.

Maintained by Sam El-Kamand. Last updated 1 years ago.

10.0 match 2 stars 2.00 score 5 scripts

swissclinicaltrialorganisation

secuTrialR:Handling of Data from the Clinical Data Management System 'secuTrial'

Seamless and standardized interaction with data exported from the clinical data management system (CDMS) 'secuTrial'<https://www.secutrial.com>. The primary data export the package works with is a standard non-rectangular export.

Maintained by Alan G. Haynes. Last updated 10 months ago.

3.4 match 8 stars 5.86 score 15 scripts

bioc

CytoDx:Robust prediction of clinical outcomes using cytometry data without cell gating

This package provides functions that predict clinical outcomes using single cell data (such as flow cytometry data, RNA single cell sequencing data) without the requirement of cell gating or clustering.

Maintained by Zicheng Hu. Last updated 5 months ago.

immunooncology cellbiology flowcytometry statisticalmethod software cellbasedassays regression classification survival

4.9 match 4.00 score 8 scripts

bioc

iPath:iPath pipeline for detecting perturbed pathways at individual level

iPath is the Bioconductor package used for calculating personalized pathway score and test the association with survival outcomes. Abundant single-gene biomarkers have been identified and used in the clinics. However, hundreds of oncogenes or tumor-suppressor genes are involved during the process of tumorigenesis. We believe individual-level expression patterns of pre-defined pathways or gene sets are better biomarkers than single genes. In this study, we devised a computational method named iPath to identify prognostic biomarker pathways, one sample at a time. To test its utility, we conducted a pan-cancer analysis across 14 cancer types from The Cancer Genome Atlas and demonstrated that iPath is capable of identifying highly predictive biomarkers for clinical outcomes, including overall survival, tumor subtypes, and tumor stage classifications. We found that pathway-based biomarkers are more robust and effective than single genes.

Maintained by Kenong Su. Last updated 5 months ago.

pathways software geneexpression survival cpp

4.3 match 2 stars 4.60 score 3 scripts

chengs94

BioPETsurv:Biomarker Prognostic Enrichment Tool for clinical trials with survival outcomes

Prognostic Enrichment is a clinical trial strategy of evaluating an intervention in a patient population with a higher rate of the unwanted clinical event than the broader patient population (R. Temple (2010) DOI:10.1038/clpt.2010.233). A higher event rate translates to a lower sample size for the clinical trial, which can have both practical and ethical advantages. This package provides tools to evaluate biomarkers for prognostic enrichment of clinical trials with survival/time-to-event outcomes.

Maintained by Si Cheng. Last updated 5 years ago.

7.3 match 2.70 score

prise6

aVirtualTwins:Adaptation of Virtual Twins Method from Jared Foster

Research of subgroups in random clinical trials with binary outcome and two treatments groups. This is an adaptation of the Jared Foster method (<https://www.ncbi.nlm.nih.gov/pubmed/21815180>).

Maintained by Francois Vieille. Last updated 7 years ago.

subgroup-identification trials

4.3 match 4 stars 4.51 score 16 scripts

medianasoft

MedianaDesigner:Power and Sample Size Calculations for Clinical Trials

Efficient simulation-based power and sample size calculations are supported for a broad class of late-stage clinical trials. The following modules are included in the package: Adaptive designs with data-driven sample size or event count re-estimation, Adaptive designs with data-driven treatment selection, Adaptive designs with data-driven population selection, Optimal selection of a futility stopping rule, Event prediction in event-driven trials, Adaptive trials with response-adaptive randomization (experimental module), Traditional trials with multiple objectives (experimental module). Traditional trials with cluster-randomized designs (experimental module).

Maintained by Alex Dmitrienko. Last updated 2 years ago.

cpp

5.0 match 20 stars 3.79 score 31 scripts

angieshen6

BayesPPD:Bayesian Power Prior Design

Bayesian power/type I error calculation and model fitting using the power prior and the normalized power prior for generalized linear models. Detailed examples of applying the package are available at <doi:10.32614/RJ-2023-016>. Models for time-to-event outcomes are implemented in the R package 'BayesPPDSurv'. The Bayesian clinical trial design methodology is described in Chen et al. (2011) <doi:10.1111/j.1541-0420.2011.01561.x>, and Psioda and Ibrahim (2019) <doi:10.1093/biostatistics/kxy009>. The normalized power prior is described in Duan et al. (2006) <doi:10.1002/env.752> and Ibrahim et al. (2015) <doi:10.1002/sim.6728>.

Maintained by Yueqi Shen. Last updated 2 months ago.

openblas cpp openmp

7.7 match 2.48 score 7 scripts 1 dependents

dami82

TCGAretriever:Retrieve Genomic and Clinical Data from CBioPortal Including TCGA Data

The Cancer Genome Atlas (TCGA) is a program aimed at improving our understanding of Cancer Biology. Several TCGA Datasets are available online. 'TCGAretriever' helps accessing and downloading TCGA data hosted on 'cBioPortal' via its Web Interface (see <https://www.cbioportal.org/> for more information).

Maintained by Damiano Fantini. Last updated 1 years ago.

4.6 match 4.11 score 26 scripts

lightbluetitan

MedDataSets:Comprehensive Medical, Disease, Treatment, and Drug Datasets

Provides an extensive collection of datasets related to medicine, diseases, treatments, drugs, and public health. This package covers topics such as drug effectiveness, vaccine trials, survival rates, infectious disease outbreaks, and medical treatments. The included datasets span various health conditions, including AIDS, cancer, bacterial infections, and COVID-19, along with information on pharmaceuticals and vaccines. These datasets are sourced from the R ecosystem and other R packages, remaining unaltered to ensure data integrity. This package serves as a valuable resource for researchers, analysts, and healthcare professionals interested in conducting medical and public health data analysis in R.

Maintained by Renzo Caceres Rossi. Last updated 5 months ago.

3.3 match 8 stars 5.68 score 60 scripts

safetygraphics

safetyCharts:Charts for Monitoring Clinical Trial Safety

Contains chart code for monitoring clinical trial safety. Charts can be used as standalone output, but are also designed for use with the 'safetyGraphics' package, which makes it easy to load data and customize the charts using an interactive web-based interface created with Shiny.

Maintained by Jeremy Wildfire. Last updated 6 months ago.

3.5 match 9 stars 5.36 score 21 scripts 1 dependents

openpharma

beeca:Binary Endpoint Estimation with Covariate Adjustment

Performs estimation of marginal treatment effects for binary outcomes when using logistic regression working models with covariate adjustment (see discussions in Magirr et al (2024) <https://osf.io/9mp58/>). Implements the variance estimators of Ge et al (2011) <doi:10.1177/009286151104500409> and Ye et al (2023) <doi:10.1080/24754269.2023.2205802>.

Maintained by Alex Przybylski. Last updated 4 months ago.

3.4 match 6 stars 5.48 score 8 scripts

alexpate30

rcprd:Extraction and Management of Clinical Practice Research Datalink Data

Simplify the process of extracting and processing Clinical Practice Research Datalink (CPRD) data in order to build datasets ready for statistical analysis. This process is difficult in 'R', as the raw data is very large and cannot be read into the R workspace. 'rcprd' utilises 'RSQLite' to create 'SQLite' databases which are stored on the hard disk. These are then queried to extract the required information for a cohort of interest, and create datasets ready for statistical analysis. The processes follow closely that from the 'rEHR' package, see Springate et al., (2017) <doi:10.1371/journal.pone.0171784>.

Maintained by Alexander Pate. Last updated 19 days ago.

3.4 match 2 stars 5.48 score 5 scripts

bioc

multiClust:multiClust: An R-package for Identifying Biologically Relevant Clusters in Cancer Transcriptome Profiles

Clustering is carried out to identify patterns in transcriptomics profiles to determine clinically relevant subgroups of patients. Feature (gene) selection is a critical and an integral part of the process. Currently, there are many feature selection and clustering methods to identify the relevant genes and perform clustering of samples. However, choosing an appropriate methodology is difficult. In addition, extensive feature selection methods have not been supported by the available packages. Hence, we developed an integrative R-package called multiClust that allows researchers to experiment with the choice of combination of methods for gene selection and clustering with ease. Using multiClust, we identified the best performing clustering methodology in the context of clinical outcome. Our observations demonstrate that simple methods such as variance-based ranking perform well on the majority of data sets, provided that the appropriate number of genes is selected. However, different gene ranking and selection methods remain relevant as no methodology works for all studies.

Maintained by Nathan Lawlor. Last updated 5 months ago.

featureextraction clustering geneexpression survival

4.2 match 4.34 score 11 scripts

bioc

MethReg:Assessing the regulatory potential of DNA methylation regions or sites on gene transcription

Epigenome-wide association studies (EWAS) detects a large number of DNA methylation differences, often hundreds of differentially methylated regions and thousands of CpGs, that are significantly associated with a disease, many are located in non-coding regions. Therefore, there is a critical need to better understand the functional impact of these CpG methylations and to further prioritize the significant changes. MethReg is an R package for integrative modeling of DNA methylation, target gene expression and transcription factor binding sites data, to systematically identify and rank functional CpG methylations. MethReg evaluates, prioritizes and annotates CpG sites with high regulatory potential using matched methylation and gene expression data, along with external TF-target interaction databases based on manually curation, ChIP-seq experiments or gene regulatory network analysis.

Maintained by Tiago Silva. Last updated 5 months ago.

methylationarray regression geneexpression epigenetics genetarget transcription

3.3 match 5 stars 5.45 score 19 scripts

eribul

decoder:Decode Coded Variables to Plain Text and the Other Way Around

Main function "decode" is used to decode coded key values to plain text. Function "code" can be used to code plain text to code if there is a 1:1 relation between the two. The concept relies on 'keyvalue' objects used for translation. There are several 'keyvalue' objects included in the areas of geographical regional codes, administrative health care unit codes, diagnosis codes and more. It is also easy to extend the use by arbitrary code sets.

Maintained by Erik Bulow. Last updated 5 years ago.

4.5 match 3.98 score 16 scripts 1 dependents

bioc

wpm:Well Plate Maker

The Well-Plate Maker (WPM) is a shiny application deployed as an R package. Functions for a command-line/script use are also available. The WPM allows users to generate well plate maps to carry out their experiments while improving the handling of batch effects. In particular, it helps controlling the "plate effect" thanks to its ability to randomize samples over multiple well plates. The algorithm for placing the samples is inspired by the backtracking algorithm: the samples are placed at random while respecting specific spatial constraints.

Maintained by Helene Borges. Last updated 5 months ago.

gui proteomics massspectrometry batcheffect experimentaldesign

3.8 match 6 stars 4.78 score 7 scripts

avi-kenny

vaccine:Statistical Tools for Immune Correlates Analysis of Vaccine Clinical Trial Data

Various semiparametric and nonparametric statistical tools for immune correlates analysis of vaccine clinical trial data. This includes calculation of summary statistics and estimation of risk, vaccine efficacy, controlled effects (controlled risk and controlled vaccine efficacy), and mediation effects (natural direct effect, natural indirect effect, proportion mediated). See Gilbert P, Fong Y, Kenny A, and Carone, M (2022) <doi:10.1093/biostatistics/kxac024> and Fay MP and Follmann DA (2023) <doi:10.48550/arXiv.2208.06465>.

Maintained by Avi Kenny. Last updated 22 days ago.

3.3 match 4 stars 5.42 score 11 scripts