moreno-betancur/survtd: Survival analysis with time-dependent covariates
Version 0.0.1

Fit semi-parametric Cox or additive hazards regression models with time- fixed covariates of any type and multiple continuous time-dependent covariates subject to missing data, measurement error or simply observed in discrete time: all these issues are handled with the two-stage Multiple Imputation for Joint Modeling (MIJM) approach developed by Moreno-Betancur et al. (2017). The package also provides easy-to-use implementations of an unadapted version (unMIJM); a simple two-stage approach (simple2S), based on single imputation of the continuous markers from linear mixed models; and the last observation carried forward (LOCF) approach for time-dependent covariates of any type, which is the traditional method to incoporate these in hazard models. Data simulation functions also included.

Getting started

Package details

AuthorMargarita Moreno-Betancur [cre, aut], Samuel L Brilleman [ctb]
MaintainerMargarita Moreno-Betancur <[email protected]>
LicenseGPL (>= 3)
Version0.0.1
Package repositoryView on GitHub
Installation Install the latest version of this package by entering the following in R:
install.packages("devtools")
library(devtools)
install_github("moreno-betancur/survtd")
moreno-betancur/survtd documentation built on Oct. 20, 2017, 5:09 a.m.