R-universe search: frailty

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 26 days ago.

fortran openmp

64.5 match 7 stars 5.56 score 1 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.

9.8 match 400 stars 20.40 score 29k scripts 3.9k dependents

kkholst

mets:Analysis of Multivariate Event Times

Implementation of various statistical models for multivariate event history data <doi:10.1007/s10985-013-9244-x>. Including multivariate cumulative incidence models <doi:10.1002/sim.6016>, and bivariate random effects probit models (Liability models) <doi:10.1016/j.csda.2015.01.014>. Modern methods for survival analysis, including regression modelling (Cox, Fine-Gray, Ghosh-Lin, Binomial regression) with fast computation of influence functions.

Maintained by Klaus K. Holst. Last updated 11 hours ago.

multivariate-time-to-event survival-analysis time-to-event fortran openblas cpp

10.1 match 14 stars 13.45 score 236 scripts 42 dependents

r-forge

copula:Multivariate Dependence with Copulas

Classes (S4) of commonly used elliptical, Archimedean, extreme-value and other copula families, as well as their rotations, mixtures and asymmetrizations. Nested Archimedean copulas, related tools and special functions. Methods for density, distribution, random number generation, bivariate dependence measures, Rosenblatt transform, Kendall distribution function, perspective and contour plots. Fitting of copula models with potentially partly fixed parameters, including standard errors. Serial independence tests, copula specification tests (independence, exchangeability, radial symmetry, extreme-value dependence, goodness-of-fit) and model selection based on cross-validation. Empirical copula, smoothed versions, and non-parametric estimators of the Pickands dependence function.

Maintained by Martin Maechler. Last updated 27 days ago.

10.7 match 11.83 score 1.2k scripts 86 dependents

tbalan

frailtyEM:Fitting Frailty Models with the EM Algorithm

Contains functions for fitting shared frailty models with a semi-parametric baseline hazard with the Expectation-Maximization algorithm. Supported data formats include clustered failures with left truncation and recurrent events in gap-time or Andersen-Gill format. Several frailty distributions, such as the the gamma, positive stable and the Power Variance Family are supported.

Maintained by Theodor Adrian Balan. Last updated 6 years ago.

frailty heterogeneity survival cpp

20.5 match 3 stars 4.69 score 33 scripts

fgaspe04

discfrail:Cox Models for Time-to-Event Data with Nonparametric Discrete Group-Specific Frailties

Functions for fitting Cox proportional hazards models for grouped time-to-event data, where the shared group-specific frailties have a discrete nonparametric distribution. There are also functions for simulating from these models, and from similar models with a parametric baseline survival function.

Maintained by Francesca Gasperoni. Last updated 6 years ago.

30.8 match 1 stars 3.00 score 8 scripts

alessandragni

TimeDepFrail:Time Dependent Shared Frailty Cox Model

Fits time-dependent shared frailty Cox model (specifically the adapted Paik et al.'s Model) based on the paper "Centre-Effect on Survival After Bone Marrow Transplantation: Application of Time-Dependent Frailty Models", by C.M. Wintrebert, H. Putter, A.H. Zwinderman and J.C. van Houwelingen (2004) <doi:10.1002/bimj.200310051>.

Maintained by Alessandra Ragni. Last updated 9 hours ago.

18.4 match 3.18 score 1 scripts

wenjie2wang

reda:Recurrent Event Data Analysis

Contains implementations of recurrent event data analysis routines including (1) survival and recurrent event data simulation from stochastic process point of view by the thinning method proposed by Lewis and Shedler (1979) <doi:10.1002/nav.3800260304> and the inversion method introduced in Cinlar (1975, ISBN:978-0486497976), (2) the mean cumulative function (MCF) estimation by the Nelson-Aalen estimator of the cumulative hazard rate function, (3) two-sample recurrent event responses comparison with the pseudo-score tests proposed by Lawless and Nadeau (1995) <doi:10.2307/1269617>, (4) gamma frailty model with spline rate function following Fu, et al. (2016) <doi:10.1080/10543406.2014.992524>.

Maintained by Wenjie Wang. Last updated 1 years ago.

mcf mean-cumulative-function recurrent-event survival-analysis cpp

6.0 match 15 stars 7.52 score 55 scripts 3 dependents

federico-rotolo

parfm:Parametric Frailty Models

Fits Parametric Frailty Models by maximum marginal likelihood. Possible baseline hazards: exponential, Weibull, inverse Weibull (Fréchet), Gompertz, lognormal, log-skew-normal, and loglogistic. Possible Frailty distributions: gamma, positive stable, inverse Gaussian and lognormal.

Maintained by Federico Rotolo. Last updated 2 years ago.

16.1 match 2.73 score 36 scripts 1 dependents

benjamin-w-campbell

fergm:Estimation and Fit Assessment of Frailty Exponential Random Graph Models

Frailty Exponential Random Graph Models estimated through pseudo likelihood with frailty terms estimated using 'Stan' as per Box-Steffensmeier et. al (2017) <doi:10.7910/DVN/K3D1M2>. Goodness of fit for Frailty Exponential Random Graph Models is also available, with easy visualizations for comparison to fit Exponential Random Graph Models.

Maintained by Benjamin W. Campbell. Last updated 3 years ago.

8.6 match 4 stars 4.86 score 18 scripts

ellessenne

rsimsum:Analysis of Simulation Studies Including Monte Carlo Error

Summarise results from simulation studies and compute Monte Carlo standard errors of commonly used summary statistics. This package is modelled on the 'simsum' user-written command in 'Stata' (White I.R., 2010 <https://www.stata-journal.com/article.html?article=st0200>), further extending it with additional performance measures and functionality.

Maintained by Alessandro Gasparini. Last updated 11 months ago.

biostatistics monte-carlo-error simulation simulation-study simulations statistics

5.3 match 28 stars 7.70 score 148 scripts

cran

joint.Cox:Joint Frailty-Copula Models for Tumour Progression and Death in Meta-Analysis

Fit survival data and perform dynamic prediction under joint frailty-copula models for tumour progression and death. Likelihood-based methods are employed for estimating model parameters, where the baseline hazard functions are modeled by the cubic M-spline or the Weibull model. The methods are applicable for meta-analytic data containing individual-patient information from several studies. Survival outcomes need information on both terminal event time (e.g., time-to-death) and non-terminal event time (e.g., time-to-tumour progression). Methodologies were published in Emura et al. (2017) <doi:10.1177/0962280215604510>, Emura et al. (2018) <doi:10.1177/0962280216688032>, Emura et al. (2020) <doi:10.1177/0962280219892295>, Shinohara et al. (2020) <doi:10.1080/03610918.2020.1855449>, Wu et al. (2020) <doi:10.1007/s00180-020-00977-1>, and Emura et al. (2021) <doi:10.1177/09622802211046390>. See also the book of Emura et al. (2019) <doi:10.1007/978-981-13-3516-7>. Survival data from ovarian cancer patients are also available.

Maintained by Takeshi Emura. Last updated 3 years ago.

13.4 match 5 stars 2.48 score 2 dependents

vmonaco

frailtySurv:General Semiparametric Shared Frailty Model

Simulates and fits semiparametric shared frailty models under a wide range of frailty distributions using a consistent and asymptotically-normal estimator. Currently supports: gamma, power variance function, log-normal, and inverse Gaussian frailty models.

Maintained by Vinnie Monaco. Last updated 2 years ago.

survival-analysis cpp

7.6 match 12 stars 4.01 score 17 scripts

sachsmc

stdReg2:Regression Standardization for Causal Inference

Contains more modern tools for causal inference using regression standardization. Four general classes of models are implemented; generalized linear models, conditional generalized estimating equation models, Cox proportional hazards models, and shared frailty gamma-Weibull models. Methodological details are described in Sjölander, A. (2016) <doi:10.1007/s10654-016-0157-3>. Also includes functionality for doubly robust estimation for generalized linear models in some special cases, and the ability to implement custom models.

Maintained by Michael C Sachs. Last updated 11 days ago.

5.8 match 2 stars 5.15 score 9 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.

5.9 match 2 stars 4.94 score 112 scripts 1 dependents

jinseob2kim

jstable:Create Tables from Different Types of Regression

Create regression tables from generalized linear model(GLM), generalized estimating equation(GEE), generalized linear mixed-effects model(GLMM), Cox proportional hazards model, survey-weighted generalized linear model(svyglm) and survey-weighted Cox model results for publication.

Maintained by Jinseob Kim. Last updated 4 days ago.

label regression table

2.8 match 28 stars 10.08 score 199 scripts 1 dependents

philipagnew

anovir:Analysis of Virulence

Epidemiological population dynamics models traditionally define a pathogen's virulence as the increase in the per capita rate of mortality of infected hosts due to infection. This package provides functions allowing virulence to be estimated by maximum likelihood techniques. The approach is based on the analysis of relative survival comparing survival in matching cohorts of infected vs. uninfected hosts (Agnew 2019) <doi:10.1101/530709>.

Maintained by Philip Agnew. Last updated 4 years ago.

8.2 match 3.34 score 20 scripts

wyattbensken

multimorbidity:Harmonizing Various Comorbidity, Multimorbidity, and Frailty Measures

Identifying comorbidities, frailty, and multimorbidity in claims and administrative data is often a duplicative process. The functions contained in this package are meant to first prepare the data to a format acceptable by all other packages, then provide a uniform and simple approach to generate comorbidity and multimorbidity metrics based on these claims data. The package is ever evolving to include new metrics, and is always looking for new measures to include. The citations used in this package include the following publications: Anne Elixhauser, Claudia Steiner, D. Robert Harris, Rosanna M. Coffey (1998) <doi:10.1097/00005650-199801000-00004>, Brian J Moore, Susan White, Raynard Washington, et al. (2017) <doi:10.1097/MLR.0000000000000735>, Mary E. Charlson, Peter Pompei, Kathy L. Ales, C. Ronald MacKenzie (1987) <doi:10.1016/0021-9681(87)90171-8>, Richard A. Deyo, Daniel C. Cherkin, Marcia A. Ciol (1992) <doi:10.1016/0895-4356(92)90133-8>, Hude Quan, Vijaya Sundararajan, Patricia Halfon, et al. (2005) <doi:10.1097/01.mlr.0000182534.19832.83>, Dae Hyun Kim, Sebastian Schneeweiss, Robert J Glynn, et al. (2018) <doi:10.1093/gerona/glx229>, Melissa Y Wei, David Ratz, Kenneth J Mukamal (2020) <doi:10.1111/jgs.16310>, Kathryn Nicholson, Amanda L. Terry, Martin Fortin, et al. (2015) <doi:10.15256/joc.2015.5.61>, Martin Fortin, José Almirall, and Kathryn Nicholson (2017)<doi:10.15256/joc.2017.7.122>.

Maintained by Wyatt Bensken. Last updated 2 years ago.

6.6 match 1 stars 3.70 score 2 scripts

dcibioinformatics

groupedSurv:Efficient Estimation of Grouped Survival Models Using the Exact Likelihood Function

These 'Rcpp'-based functions compute the efficient score statistics for grouped time-to-event data (Prentice and Gloeckler, 1978), with the optional inclusion of baseline covariates. Functions for estimating the parameter of interest and nuisance parameters, including baseline hazards, using maximum likelihood are also provided. A parallel set of functions allow for the incorporation of family structure of related individuals (e.g., trios). Note that the current implementation of the frailty model (Ripatti and Palmgren, 2000) is sensitive to departures from model assumptions, and should be considered experimental. For these data, the exact proportional-hazards-model-based likelihood is computed by evaluating multiple variable integration. The integration is accomplished using the 'Cuba' library (Hahn, 2005), and the source files are included in this package. The maximization process is carried out using Brent's algorithm, with the C++ code file from John Burkardt and John Denker (Brent, 2002).

Maintained by Alexander Sibley. Last updated 2 years ago.

cpp openmp

11.7 match 2.00 score 3 scripts

goranbrostrom

eha:Event History Analysis

Parametric proportional hazards fitting with left truncation and right censoring for common families of distributions, piecewise constant hazards, and discrete models. Parametric accelerated failure time models for left truncated and right censored data. Proportional hazards models for tabular and register data. Sampling of risk sets in Cox regression, selections in the Lexis diagram, bootstrapping. Broström (2022) <doi:10.1201/9780429503764>.

Maintained by Göran Broström. Last updated 10 months ago.

fortran openblas

2.3 match 7 stars 9.76 score 308 scripts 10 dependents

arvsjo

stdReg:Regression Standardization

Contains functionality for regression standardization. Four general classes of models are allowed; generalized linear models, conditional generalized estimating equation models, Cox proportional hazards models and shared frailty gamma-Weibull models. Sjolander, A. (2016) <doi:10.1007/s10654-016-0157-3>.

Maintained by Arvid Sjolander. Last updated 4 years ago.

7.7 match 2.80 score 53 scripts 1 dependents

cran

bcfrailph:Semiparametric Bivariate Correlated Frailty Models Fit

Fit and simulate a semiparametric bivariate correlated frailty models with proportional hazard structure. Frailty distributions, such as gamma and lognormal models are supported. Bivariate gamma fit is obtained using the approach given in Iachine (1995) and lognormal fit is based on the approach by Ripatti and Palmgren (2000) <doi:10.1111/j.0006-341X.2000.01016.x>.

Maintained by Mesfin Tsegaye. Last updated 2 years ago.

14.0 match 1.48 score 1 dependents

heilokchow

frailtyMMpen:Efficient Algorithm for High-Dimensional Frailty Model

The penalized and non-penalized Minorize-Maximization (MM) method for frailty models to fit the clustered data, multi-event data and recurrent data. Least absolute shrinkage and selection operator (LASSO), minimax concave penalty (MCP) and smoothly clipped absolute deviation (SCAD) penalized functions are implemented. All the methods are computationally efficient. These general methods are proposed based on the following papers, Huang, Xu and Zhou (2022) <doi:10.3390/math10040538>, Huang, Xu and Zhou (2023) <doi:10.1177/09622802221133554>.

Maintained by Yunpeng Zhou. Last updated 2 years ago.

gsl cpp

6.7 match 2.70 score

swihart

repeated:Non-Normal Repeated Measurements Models

Various functions to fit models for non-normal repeated measurements, such as Binary Random Effects Models with Two Levels of Nesting, Bivariate Beta-binomial Regression Models, Marginal Bivariate Binomial Regression Models, Cormack capture-recapture models, Continuous-time Hidden Markov Chain Models, Discrete-time Hidden Markov Chain Models, Changepoint Location Models using a Continuous-time Two-state Hidden Markov Chain, generalized nonlinear autoregression models, multivariate Gaussian copula models, generalized non-linear mixed models with one random effect, generalized non-linear mixed models using h-likelihood for one random effect, Repeated Measurements Models for Counts with Frailty or Serial Dependence, Repeated Measurements Models for Continuous Variables with Frailty or Serial Dependence, Ordinal Random Effects Models with Dropouts, marginal homogeneity models for square contingency tables, correlated negative binomial models with Kalman update. References include Lindsey's text books, JK Lindsey (2001) <isbn:10-0198508123> and JK Lindsey (1999) <isbn:10-0198505590>.

Maintained by Bruce Swihart. Last updated 6 months ago.

fortran

4.2 match 3.97 score 31 scripts

cran

spass:Study Planning and Adaptation of Sample Size

Sample size estimation and blinded sample size reestimation in Adaptive Study Design.

Maintained by Marius Placzek. Last updated 4 years ago.

cpp

10.8 match 1.30 score

msnoh1

frailtyHL:Frailty Models via Hierarchical Likelihood

Implements the h-likelihood estimation procedures for general frailty models including competing-risk models and joint models.

Maintained by Maengseok Noh. Last updated 5 years ago.

13.3 match 1.04 score 11 scripts

robindenz1

adjustedCurves:Confounder-Adjusted Survival Curves and Cumulative Incidence Functions

Estimate and plot confounder-adjusted survival curves using either 'Direct Adjustment', 'Direct Adjustment with Pseudo-Values', various forms of 'Inverse Probability of Treatment Weighting', two forms of 'Augmented Inverse Probability of Treatment Weighting', 'Empirical Likelihood Estimation' or 'Targeted Maximum Likelihood Estimation'. Also includes a significance test for the difference between two adjusted survival curves and the calculation of adjusted restricted mean survival times. Additionally enables the user to estimate and plot cause-specific confounder-adjusted cumulative incidence functions in the competing risks setting using the same methods (with some exceptions). For details, see Denz et. al (2023) <doi:10.1002/sim.9681>.

Maintained by Robin Denz. Last updated 1 months ago.

adjusted confidence-intervals cumulative-incidence survival-curves

1.6 match 37 stars 7.93 score 93 scripts

cran

extrafrail:Estimation and Additional Tools for Alternative Shared Frailty Models

Provide estimation and data generation tools for some new multivariate frailty models. This version includes the gamma, inverse Gaussian, weighted Lindley, Birnbaum-Saunders, truncated normal, mixture of inverse Gaussian and mixture of Birnbaum-Saunders as the distribution for the frailty terms. For the basal model, it is considered a parametric approach based on the exponential, Weibull and the piecewise exponential distributions as well as a semiparametric approach. For details, see Gallardo and Bourguignon (2022) <doi:10.48550/arXiv.2206.12973> and Gallardo et al. (2024) <doi:10.1007/s11222-024-10458-w>.

Maintained by Diego Gallardo. Last updated 6 months ago.

7.7 match 1.60 score

fndemarqui

rsurv:Random Generation of Survival Data

Random generation of survival data from a wide range of regression models, including accelerated failure time (AFT), proportional hazards (PH), proportional odds (PO), accelerated hazard (AH), Yang and Prentice (YP), and extended hazard (EH) models. The package 'rsurv' also stands out by its ability to generate survival data from an unlimited number of baseline distributions provided that an implementation of the quantile function of the chosen baseline distribution is available in R. Another nice feature of the package 'rsurv' lies in the fact that linear predictors are specified via a formula-based approach, facilitating the inclusion of categorical variables and interaction terms. The functions implemented in the package 'rsurv' can also be employed to simulate survival data with more complex structures, such as survival data with different types of censoring mechanisms, survival data with cure fraction, survival data with random effects (frailties), multivariate survival data, and competing risks survival data. Details about the R package 'rsurv' can be found in Demarqui (2024) <doi:10.48550/arXiv.2406.01750>.

Maintained by Fabio Demarqui. Last updated 2 months ago.

2.5 match 1 stars 4.56 score 24 scripts

hoarzpassey

PenCoxFrail:Regularization in Cox Frailty Models

Different regularization approaches for Cox Frailty Models by penalization methods are provided.

Maintained by Andreas Groll. Last updated 9 months ago.

openblas cpp

10.9 match 1.00 score 6 scripts

cran

di:Deficit Index (DI)

A set of utilities for calculating the Deficit (frailty) Index (DI) in gerontological studies. The deficit index was first proposed by Arnold Mitnitski and Kenneth Rockwood and represents a proxy measure of aging and also can be served as a sensitive predictor of survival. For more information, see (i)"Accumulation of Deficits as a Proxy Measure of Aging" by Arnold B. Mitnitski et al. (2001), The Scientific World Journal 1, <DOI:10.1100/tsw.2001.58>; (ii) "Frailty, fitness and late-life mortality in relation to chronological and biological age" by Arnold B Mitnitski et al. (2001), BMC Geriatrics2002 2(1), <DOI:10.1186/1471-2318-2-1>.

Maintained by Ilya Y. Zhbannikov. Last updated 7 years ago.

3.5 match 3.00 score

fauvernierma

survPen:Multidimensional Penalized Splines for (Excess) Hazard Models, Relative Mortality Ratio Models and Marginal Intensity Models

Fits (excess) hazard, relative mortality ratio or marginal intensity models with multidimensional penalized splines allowing for time-dependent effects, non-linear effects and interactions between several continuous covariates. In survival and net survival analysis, in addition to modelling the effect of time (via the baseline hazard), one has often to deal with several continuous covariates and model their functional forms, their time-dependent effects, and their interactions. Model specification becomes therefore a complex problem and penalized regression splines represent an appealing solution to that problem as splines offer the required flexibility while penalization limits overfitting issues. Current implementations of penalized survival models can be slow or unstable and sometimes lack some key features like taking into account expected mortality to provide net survival and excess hazard estimates. In contrast, survPen provides an automated, fast, and stable implementation (thanks to explicit calculation of the derivatives of the likelihood) and offers a unified framework for multidimensional penalized hazard and excess hazard models. Later versions (>2.0.0) include penalized models for relative mortality ratio, and marginal intensity in recurrent event setting. survPen may be of interest to those who 1) analyse any kind of time-to-event data: mortality, disease relapse, machinery breakdown, unemployment, etc 2) wish to describe the associated hazard and to understand which predictors impact its dynamics, 3) wish to model the relative mortality ratio between a cohort and a reference population, 4) wish to describe the marginal intensity for recurrent event data. See Fauvernier et al. (2019a) <doi:10.21105/joss.01434> for an overview of the package and Fauvernier et al. (2019b) <doi:10.1111/rssc.12368> for the method.

Maintained by Mathieu Fauvernier. Last updated 4 months ago.

cpp

1.5 match 12 stars 6.82 score 85 scripts 1 dependents

atanubhattacharjee

SurviMChd:High Dimensional Survival Data Analysis with Markov Chain Monte Carlo

High dimensional survival data analysis with Markov Chain Monte Carlo(MCMC). Currently supports frailty data analysis. Allows for Weibull and Exponential distribution. Includes function for interval censored data.

Maintained by Atanu Bhattacharjee. Last updated 1 years ago.

jags cpp

9.6 match 1.00 score

zifangkong

FunSurv:Modeling Time-to-Event Data with Functional Predictors

A collection of methods for modeling time-to-event data using both functional and scalar predictors. It implements functional data analysis techniques for estimation and inference, allowing researchers to assess the impact of functional covariates on survival outcomes, including time-to-single event and recurrent event outcomes.

Maintained by Zifang Kong. Last updated 19 days ago.

2.7 match 3.40 score

cran

bcfrailphdv:Bivariate Correlated Frailty Models with Varied Variances

Fit and simulate bivariate correlated frailty models with proportional hazard structure. Frailty distributions, such as gamma and lognormal models are supported for semiparametric procedures. Frailty variances of the two subjects can be varied or equal. Details on the models are available in book of Wienke (2011,ISBN:978-1-4200-7388-1). Bivariate gamma fit is obtained using the approach given in Iachine (1995) with modifications. Lognormal fit is based on the approach by Ripatti and Palmgren (2000) <doi:10.1111/j.0006-341X.2000.01016.x>. Frailty distributions, such as gamma, inverse gaussian and power variance frailty models are supported for parametric approach.

Maintained by Mesfin Tsegaye. Last updated 2 years ago.

9.2 match 1.00 score

cardiomoon

autoReg:Automatic Linear and Logistic Regression and Survival Analysis

Make summary tables for descriptive statistics and select explanatory variables automatically in various regression models. Support linear models, generalized linear models and cox-proportional hazard models. Generate publication-ready tables summarizing result of regression analysis and plots. The tables and plots can be exported in "HTML", "pdf('LaTex')", "docx('MS Word')" and "pptx('MS Powerpoint')" documents.

Maintained by Keon-Woong Moon. Last updated 1 years ago.

1.3 match 49 stars 7.13 score 69 scripts

wonderzhm

spBayesSurv:Bayesian Modeling and Analysis of Spatially Correlated Survival Data

Provides several Bayesian survival models for spatial/non-spatial survival data: proportional hazards (PH), accelerated failure time (AFT), proportional odds (PO), and accelerated hazards (AH), a super model that includes PH, AFT, PO and AH as special cases, Bayesian nonparametric nonproportional hazards (LDDPM), generalized accelerated failure time (GAFT), and spatially smoothed Polya tree density estimation. The spatial dependence is modeled via frailties under PH, AFT, PO, AH and GAFT, and via copulas under LDDPM and PH. Model choice is carried out via the logarithm of the pseudo marginal likelihood (LPML), the deviance information criterion (DIC), and the Watanabe-Akaike information criterion (WAIC). See Zhou, Hanson and Zhang (2020) <doi:10.18637/jss.v092.i09>.

Maintained by Haiming Zhou. Last updated 1 years ago.

openblas cpp

4.3 match 2 stars 2.03 score 54 scripts

lzumeta

injurytools:A Toolkit for Sports Injury and Illness Data Analysis

Sports Injury Data analysis aims to identify and describe the magnitude of the injury problem, and to gain more insights (e.g. determine potential risk factors) by statistical modelling approaches. The 'injurytools' package provides standardized routines and utilities that simplify such analyses. It offers functions for data preparation, informative visualizations and descriptive and model-based analyses.

Maintained by Lore Zumeta Olaskoaga. Last updated 6 months ago.

sports sports-injuries

1.3 match 5 stars 6.31 score 27 scripts

swihart

event:Event History Procedures and Models

Functions for setting up and analyzing event history data.

Maintained by Bruce Swihart. Last updated 8 years ago.

fortran

1.8 match 1 stars 4.74 score 548 scripts

shanpengli

PDXpower:Time to Event Outcome in Experimental Designs of Pre-Clinical Studies

Conduct simulation-based customized power calculation for clustered time to event data in a mixed crossed/nested design, where a number of cell lines and a number of mice within each cell line are considered to achieve a desired statistical power, motivated by Eckel-Passow and colleagues (2021) <doi:10.1093/neuonc/noab137> and Li and colleagues (2024) <doi:10.48550/arXiv.2404.08927>. This package provides two commonly used models for powering a design, linear mixed effects and Cox frailty model. Both models account for within-subject (cell line) correlation while holding different distributional assumptions about the outcome. Alternatively, the counterparts of fixed effects model are also available, which produces similar estimates of statistical power.

Maintained by Shanpeng Li. Last updated 2 months ago.

2.1 match 1 stars 3.40 score 2 scripts

mclements

rstpm2:Smooth Survival Models, Including Generalized Survival Models

R implementation of generalized survival models (GSMs), smooth accelerated failure time (AFT) models and Markov multi-state models. For the GSMs, g(S(t|x))=eta(t,x) for a link function g, survival S at time t with covariates x and a linear predictor eta(t,x). The main assumption is that the time effect(s) are smooth <doi:10.1177/0962280216664760>. For fully parametric models with natural splines, this re-implements Stata's 'stpm2' function, which are flexible parametric survival models developed by Royston and colleagues. We have extended the parametric models to include any smooth parametric smoothers for time. We have also extended the model to include any smooth penalized smoothers from the 'mgcv' package, using penalized likelihood. These models include left truncation, right censoring, interval censoring, gamma frailties and normal random effects <doi:10.1002/sim.7451>, and copulas. For the smooth AFTs, S(t|x) = S_0(t*eta(t,x)), where the baseline survival function S_0(t)=exp(-exp(eta_0(t))) is modelled for natural splines for eta_0, and the time-dependent cumulative acceleration factor eta(t,x)=\int_0^t exp(eta_1(u,x)) du for log acceleration factor eta_1(u,x). The Markov multi-state models allow for a range of models with smooth transitions to predict transition probabilities, length of stay, utilities and costs, with differences, ratios and standardisation.

Maintained by Mark Clements. Last updated 5 months ago.

fortran openblas cpp

0.5 match 27 stars 11.09 score 137 scripts 52 dependents

scheike

timereg:Flexible Regression Models for Survival Data

Programs for Martinussen and Scheike (2006), `Dynamic Regression Models for Survival Data', Springer Verlag. Plus more recent developments. Additive survival model, semiparametric proportional odds model, fast cumulative residuals, excess risk models and more. Flexible competing risks regression including GOF-tests. Two-stage frailty modelling. PLS for the additive risk model. Lasso in the 'ahaz' package.

Maintained by Thomas Scheike. Last updated 7 months ago.

openblas

0.5 match 31 stars 10.42 score 289 scripts 44 dependents

stc04003

reReg:Recurrent Event Regression

A comprehensive collection of practical and easy-to-use tools for regression analysis of recurrent events, with or without the presence of a (possibly) informative terminal event described in Chiou et al. (2023) <doi:10.18637/jss.v105.i05>. The modeling framework is based on a joint frailty scale-change model, that includes models described in Wang et al. (2001) <doi:10.1198/016214501753209031>, Huang and Wang (2004) <doi:10.1198/016214504000001033>, Xu et al. (2017) <doi:10.1080/01621459.2016.1173557>, and Xu et al. (2019) <doi:10.5705/SS.202018.0224> as special cases. The implemented estimating procedure does not require any parametric assumption on the frailty distribution. The package also allows the users to specify different model forms for both the recurrent event process and the terminal event.

Maintained by Sy Han (Steven) Chiou. Last updated 3 months ago.

openblas cpp

0.8 match 23 stars 6.35 score 36 scripts 1 dependents

atanubhattacharjee

dscoreMSM:Survival Proximity Score Matching in Multi-State Survival Model

Implements survival proximity score matching in multi-state survival models. Includes tools for simulating survival data and estimating transition-specific coxph models with frailty terms. The primary methodological work on multistate censored data modeling using propensity score matching has been published by Bhattacharjee et al.(2024) <doi:10.1038/s41598-024-54149-y>.

Maintained by Atanu Bhattacharjee. Last updated 4 months ago.

jags cpp

2.3 match 2.00 score

cran

stpm:Stochastic Process Model for Analysis of Longitudinal and Time-to-Event Outcomes

Utilities to estimate parameters of the models with survival functions induced by stochastic covariates. Miscellaneous functions for data preparation and simulation are also provided. For more information, see: (i)"Stochastic model for analysis of longitudinal data on aging and mortality" by Yashin A. et al. (2007), Mathematical Biosciences, 208(2), 538-551, <DOI:10.1016/j.mbs.2006.11.006>; (ii) "Health decline, aging and mortality: how are they related?" by Yashin A. et al. (2007), Biogerontology 8(3), 291(302), <DOI:10.1007/s10522-006-9073-3>.

Maintained by Ilya Y. Zhbannikov. Last updated 3 years ago.

openblas cpp openmp

1.6 match 2.70 score

cran

AF:Model-Based Estimation of Confounder-Adjusted Attributable Fractions

Estimates the attributable fraction in different sampling designs adjusted for measured confounders using logistic regression (cross-sectional and case-control designs), conditional logistic regression (matched case-control design), Cox proportional hazard regression (cohort design with time-to- event outcome), gamma-frailty model with a Weibull baseline hazard and instrumental variables analysis. An exploration of the AF with a genetic exposure can be found in the package 'AFheritability' Dahlqwist E et al. (2019) <doi:10.1007/s00439-019-02006-8>.

Maintained by Elisabeth Dahlqwist. Last updated 6 years ago.

2.1 match 2.00 score

cran

MMAD:MM Algorithm Based on the Assembly-Decomposition Technology

The Minorize-Maximization(MM) algorithm based on Assembly-Decomposition(AD) technology can be used for model estimation of parametric models, semi-parametric models and non-parametric models. We selected parametric models including left truncated normal distribution, type I multivariate zero-inflated generalized poisson distribution and multivariate compound zero-inflated generalized poisson distribution; semiparametric models include Cox model and gamma frailty model; nonparametric model is estimated for type II interval-censored data. These general methods are proposed based on the following papers, Tian, Huang and Xu (2019) <doi:10.5705/SS.202016.0488>, Huang, Xu and Tian (2019) <doi:10.5705/ss.202016.0516>, Zhang and Huang (2022) <doi:10.1117/12.2642737>.

Maintained by Dengge Liu. Last updated 2 years ago.

3.9 match 1.00 score

jjr1234

PJFM:Variational Inference for High-Dimensional Joint Frailty Model

Joint frailty models have been widely used to study the associations between recurrent events and a survival outcome. However, existing joint frailty models only consider one or a few recurrent events and cannot deal with high-dimensional recurrent events. This package can be used to fit our recently developed penalized joint frailty model that can handle high-dimensional recurrent events. Specifically, an adaptive lasso penalty is imposed on the parameters for the effects of the recurrent events on the survival outcome, which allows for variable selection. Also, our algorithm is computationally efficient, which is based on the Gaussian variational approximation method.

Maintained by Jiehuan Sun. Last updated 5 months ago.

openblas cpp

3.8 match 1.00 score

cran

coxme:Mixed Effects Cox Models

Fit Cox proportional hazards models containing both fixed and random effects. The random effects can have a general form, of which familial interactions (a "kinship" matrix) is a particular special case. Note that the simplest case of a mixed effects Cox model, i.e. a single random per-group intercept, is also called a "frailty" model. The approach is based on Ripatti and Palmgren, Biometrics 2002.

Maintained by Terry M. Therneau. Last updated 7 months ago.

0.5 match 2 stars 6.60 score 15 dependents

lduchate

UdderQuarterInfectionData:Udder Quarter Infection Data

The udder quarter infection data set contains infection times of individual cow udder quarters with Corynebacterium bovis (Laevens et al. 1997 <DOI:10.3168/jds.S0022-0302(97)76295-7>). Obviously, the four udder quarters are clustered within a cow, and udder quarters are sampled only approximately monthly, generating interval-censored data. The data set contains both covariates that change within a cow (e.g., front and rear udder quarters) and covariates that change between cows (e.g., parity [the number of previous calvings]). The correlation between udder infection times within a cow also is of interest, because this is a measure of the infectivity of the agent causing the disease. Various models have been applied to address the problem of interdependence for right-censored event times. These models, as applied to this data set, can be found back in the publications found in the reference list.

Maintained by Luc Duchateau. Last updated 8 years ago.

1.9 match 1.00 score 8 scripts

keisuke-hanada

rmstBayespara:Bayesian Restricted Mean Survival Time for Cluster Effect

The parametric Bayes analysis for the restricted mean survival time (RMST) with cluster effect, as described in Hanada and Kojima (2024) <doi:10.48550/arXiv.2406.06071>. Bayes estimation with random-effect and frailty-effect can be applied to several parametric models useful in survival time analysis. The RMST under these parametric models can be computed from the obtained posterior samples.

Maintained by Keisuke Hanada. Last updated 9 months ago.

0.5 match 1 stars 3.00 score

gabrielgrandemagne

CureDepCens:Dependent Censoring Regression Models with Cure Fraction

Cure dependent censoring regression models for long-term survival multivariate data. These models are based on extensions of the frailty models, capable to accommodating the cure fraction and the dependence between failure and censoring times, with Weibull and piecewise exponential marginal distributions. Theoretical details regarding the models implemented in the package can be found in Schneider et al. (2022) <doi:10.1007/s10651-022-00549-0>.

Maintained by Silvana Schneider. Last updated 2 years ago.

0.5 match 2.74 score 11 scripts

gabrielgrandemagne

DepCens:Dependent Censoring Regression Models

Dependent censoring regression models for survival multivariate data. These models are based on extensions of the frailty models, capable to accommodating the dependence between failure and censoring times, with Weibull and piecewise exponential marginal distributions. Theoretical details regarding the models implemented in the package can be found in Schneider et al. (2019) <doi:10.1002/bimj.201800391>.

Maintained by Silvana Schneider. Last updated 2 years ago.

0.5 match 1 stars 2.70 score 7 scripts

pcalhoun1

MST:Multivariate Survival Trees

Constructs trees for multivariate survival data using marginal and frailty models. Grows, prunes, and selects the best-sized tree.

Maintained by Peter Calhoun. Last updated 5 years ago.

0.5 match 2.00 score 6 scripts

mclements

nltm:Non-Linear Transformation Models

Fits a non-linear transformation model ('nltm') for analyzing survival data, see Tsodikov (2003) <doi:10.1111/1467-9868.00414>. The class of 'nltm' includes the following currently supported models: Cox proportional hazard, proportional hazard cure, proportional odds, proportional hazard - proportional hazard cure, proportional hazard - proportional odds cure, Gamma frailty, and proportional hazard - proportional odds.

Maintained by Mark Clements. Last updated 3 years ago.

cpp

0.5 match 2.00 score 1 scripts

cran

mexhaz:Mixed Effect Excess Hazard Models

Fit flexible (excess) hazard regression models with the possibility of including non-proportional effects of covariables and of adding a random effect at the cluster level (corresponding to a shared frailty). A detailed description of the package functionalities is provided in Charvat and Belot (2021) <doi: 10.18637/jss.v098.i14>.

Maintained by Hadrien Charvat. Last updated 9 months ago.

0.5 match 1.78 score 1 dependents

cran

PICBayes:Bayesian Models for Partly Interval-Censored Data

Contains functions to fit proportional hazards (PH) model to partly interval-censored (PIC) data (Pan et al. (2020) <doi:10.1177/0962280220921552>), PH model with spatial frailty to spatially dependent PIC data (Pan and Cai (2021) <doi:10.1080/03610918.2020.1839497>), and mixed effects PH model to clustered PIC data. Each random intercept/random effect can follow both a normal prior and a Dirichlet process mixture prior. It also includes the corresponding functions for general interval-censored data.

Maintained by Chun Pan. Last updated 4 years ago.

0.5 match 1.00 score