Package: RISCA
Type: Package
Title: Causal Inference and Prediction in Cohort-Based Analyses
Version: 0.8.2
Depends: R (>= 2.10), splines, survival, relsurv
Imports: date, graphics, nlme, MASS, mvtnorm, statmod, parallel,
        doParallel, foreach, nnet, kernlab, glmnet, caret, SuperLearner
Authors@R: c(
    person("Yohann", "Foucher", email = "Yohann.Foucher@univ-nantes.fr", role = c("aut", "cre"), comment = c(ORCID = "0000-0003-0330-7457")),
    person("Florent", "Le Borgne", email = "fleborgne@idbc.fr", role = "aut"),
	person("Etienne", "Dantan", email = "Etienne.Dantan@univ-nantes.fr", role = "aut"),
    person("Florence", "Gillaizeau", email = "Florence.Gillaizeau@univ-nantes.fr", role = "aut"),
	person("Arthur", "Chatton", email = "arthur.chatton@etu.univ-nantes.fr", role = "aut"),
	person("Christophe", "Combescure", email = "christophe.combescure@hcuge.ch", role = "aut"))
Description: We propose 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 (Le Borgne, 2016, <doi:10.1002/sim.6777>), competing events (Trebern-Launay, 2018, <doi: 10.1007/s10654-017-0322-3>), and multi-state data (Gillaizeau, 2018, <doi: 10.1002/sim.7550>). For multistate data, semi-Markov model with interval censoring (Foucher, 2008, <doi: 10.1177/0962280208093889>) may be considered and we propose the possibility to consider the excess of mortality related to the disease compared to reference lifetime tables (Gillaizeau, 2017, <doi: 10.1177/0962280215586456>). 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 (Le Borgne, 2018,  <doi: 10.1177/0962280217702416>). Finally, several functions are available to assess time-dependent ROC curves (Combescure, 2017, <doi: 10.1177/0962280212464542>) or survival curves (Combescure, 2014, <doi: 10.1002/sim.6111>) from aggregated data.
License: GPL (>= 2)
LazyLoad: yes
URL: www.labcom-risca.com
NeedsCompilation: no
Packaged: 2020-04-04 17:01:15 UTC; foucher-y
Author: Yohann Foucher [aut, cre] (<https://orcid.org/0000-0003-0330-7457>),
  Florent Le Borgne [aut],
  Etienne Dantan [aut],
  Florence Gillaizeau [aut],
  Arthur Chatton [aut],
  Christophe Combescure [aut]
Maintainer: Yohann Foucher <Yohann.Foucher@univ-nantes.fr>
Repository: CRAN
Date/Publication: 2020-04-05 00:00:06 UTC
Built: R 3.5.3; ; 2020-04-22 08:55:53 UTC; windows
