Showing 8 of total 8 results (show query)
openpharma
mmrm:Mixed Models for Repeated Measures
Mixed models for repeated measures (MMRM) are a popular choice for analyzing longitudinal continuous outcomes in randomized clinical trials and beyond; see Cnaan, Laird and Slasor (1997) <doi:10.1002/(SICI)1097-0258(19971030)16:20%3C2349::AID-SIM667%3E3.0.CO;2-E> for a tutorial and Mallinckrodt, Lane, Schnell, Peng and Mancuso (2008) <doi:10.1177/009286150804200402> for a review. This package implements MMRM based on the marginal linear model without random effects using Template Model Builder ('TMB') which enables fast and robust model fitting. Users can specify a variety of covariance matrices, weight observations, fit models with restricted or standard maximum likelihood inference, perform hypothesis testing with Satterthwaite or Kenward-Roger adjustment, and extract least square means estimates by using 'emmeans'.
Maintained by Daniel Sabanes Bove. Last updated 9 days ago.
86.4 match 138 stars 12.15 score 113 scripts 4 dependentsopenpharma
brms.mmrm:Bayesian MMRMs using 'brms'
The mixed model for repeated measures (MMRM) is a popular model for longitudinal clinical trial data with continuous endpoints, and 'brms' is a powerful and versatile package for fitting Bayesian regression models. The 'brms.mmrm' R package leverages 'brms' to run MMRMs, and it supports a simplified interfaced to reduce difficulty and align with the best practices of the life sciences. References: Bürkner (2017) <doi:10.18637/jss.v080.i01>, Mallinckrodt (2008) <doi:10.1177/009286150804200402>.
Maintained by William Michael Landau. Last updated 5 months ago.
brmslife-sciencesmc-stanmmrmstanstatistics
65.6 match 21 stars 8.80 score 13 scripts
insightsengineering
tern.mmrm:Tables and Graphs for Mixed Models for Repeated Measures (MMRM)
Mixed models for repeated measures (MMRM) are a popular choice for analyzing longitudinal continuous outcomes in randomized clinical trials and beyond; see for example Cnaan, Laird and Slasor (1997) <doi:10.1002/(SICI)1097-0258(19971030)16:20%3C2349::AID-SIM667%3E3.0.CO;2-E>. This package provides an interface for fitting MMRM within the 'tern' <https://cran.r-project.org/package=tern> framework by Zhu et al. (2023) and tabulate results easily using 'rtables' <https://cran.r-project.org/package=rtables> by Becker et al. (2023). It builds on 'mmrm' <https://cran.r-project.org/package=mmrm> by Sabanés Bové et al. (2023) for the actual MMRM computations.
Maintained by Joe Zhu. Last updated 6 months ago.
graphslistingsstatistical-engineeringtables
63.0 match 6 stars 7.26 score 8 scripts 1 dependentsinsightsengineering
rbmi:Reference Based Multiple Imputation
Implements standard and reference based multiple imputation methods for continuous longitudinal endpoints (Gower-Page et al. (2022) <doi:10.21105/joss.04251>). In particular, this package supports deterministic conditional mean imputation and jackknifing as described in Wolbers et al. (2022) <doi:10.1002/pst.2234>, Bayesian multiple imputation as described in Carpenter et al. (2013) <doi:10.1080/10543406.2013.834911>, and bootstrapped maximum likelihood imputation as described in von Hippel and Bartlett (2021) <doi: 10.1214/20-STS793>.
Maintained by Isaac Gravestock. Last updated 23 days ago.
11.6 match 18 stars 8.78 score 33 scripts 1 dependents
mcdonohue
longpower:Sample Size Calculations for Longitudinal Data
Compute power and sample size for linear models of longitudinal data. Supported models include mixed-effects models and models fit by generalized least squares and generalized estimating equations. The package is described in Iddi and Donohue (2022) <DOI:10.32614/RJ-2022-022>. Relevant formulas are derived by Liu and Liang (1997) <DOI:10.2307/2533554>, Diggle et al (2002) <ISBN:9780199676750>, and Lu, Luo, and Chen (2008) <DOI:10.2202/1557-4679.1098>.
Maintained by Michael C. Donohue. Last updated 6 months ago.
4.3 match 3 stars 6.04 score 22 scripts 1 dependents
johnnyzhz
WebPower:Basic and Advanced Statistical Power Analysis
This is a collection of tools for conducting both basic and advanced statistical power analysis including correlation, proportion, t-test, one-way ANOVA, two-way ANOVA, linear regression, logistic regression, Poisson regression, mediation analysis, longitudinal data analysis, structural equation modeling and multilevel modeling. It also serves as the engine for conducting power analysis online at <https://webpower.psychstat.org>.
Maintained by Zhiyong Zhang. Last updated 6 months ago.
4.3 match 8 stars 5.52 score 128 scriptsinsightsengineering
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-trialsmodulesnestoutputsshiny
1.7 match 34 stars 10.25 score 149 scripts
