R is a free software environment for statistical computing and graphics. It compiles and runs on a wide variety of UNIX platforms, Windows and MacOS. To download R, please click on this link and choose your preferred CRAN mirror.

In observational studies, the presence of confounding factors is common and the comparison of different groups of subjects requires adjustment. In this package, we propose to R-users simple functions to estimate adjusted survival curves and log-rank test based on inverse probability weighting (IPW).

Download the IPWsurvival package and save the resulting file to your machine

Install the IPWsurvival package

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R is a free software environment for statistical computing and graphics. It compiles and runs on a wide variety of UNIX platforms, Windows and MacOS. To download R, please click on this link and choose your preferred CRAN mirror.

The package Multistate have been developped to implement multistate models. We propose functions implementing 3-state and 4-state models such as Semi-Markov (SM) models (modelling the probabilities of transitions according to the times spent in states) and time-inhomogenous Markov models (modelling the probabilities of transitions according to the chronological times). We also propose to take into account the mortality of the general population (relative survival approach).
The methods proposed are limited to parametric models. Estimation of the parameters is based on maximum likelihood.

As an example, we proposed a data frame with the data extracted from the DIVAT data bank with a multistate possible evolution of the clinical status of kidney transplant recipients.

Download the Multistate package and save the resulting file to your machine

R is a free software environment for statistical computing and graphics. It compiles and runs on a wide variety of UNIX platforms, Windows and MacOS. To download R, please click on this link and choose your preferred CRAN mirror.

The purpose of this package is to study the heterogeneity of risk factors between two groups of patients. Missing data are not allowed. Estimation of the parameters is based on partial likelihood maximization and Monte-Carlo simulations associated with bootstrap re-sampling yields to obtain the corresponding standard deviations.
As an example, we proposed a data frame with the data extracted from the DIVAT data bank for first kidney transplant recipients (reference group) and second kidney transplant recipients (relative group).

Download the MRsurv package and save the resulting file to your machine

Install the MRsurv package