RISCA: Causal Inference and Prediction in Cohort-Based Analyses

Numerous functions for cohort-based analyses, either for prediction or causal inference. For causal inference, it includes Inverse Probability Weighting and G-computation for marginal estimation of an exposure effect when confounders are expected. We deal with binary outcomes, times-to-events, competing events, and multi-state data. For multistate data, semi-Markov model with interval censoring may be considered, and we propose the possibility to consider the excess of mortality related to the disease compared to reference lifetime tables. For predictive studies, we propose a set of functions to estimate time-dependent receiver operating characteristic (ROC) curves with the possible consideration of right-censoring times-to-events or the presence of confounders. Finally, several functions are available to assess time-dependent ROC curves or survival curves from aggregated data.

Getting started

Package details

AuthorYohann Foucher [aut, cre] (<https://orcid.org/0000-0003-0330-7457>), Florent Le Borgne [aut], Arthur Chatton [aut]
MaintainerYohann Foucher <Yohann.Foucher@univ-poitiers.fr>
LicenseGPL (>= 2)
Version1.0.5
Package repositoryView on CRAN
Installation Install the latest version of this package by entering the following in R:
install.packages("RISCA")

Try the RISCA package in your browser

Any scripts or data that you put into this service are public.

RISCA documentation built on June 22, 2024, 12:22 p.m.