RISCA: Causal Inference and Prediction in Cohort-Based Analyses

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-dependant ROC curves (Combescure, 2017, <doi:10.1177/0962280212464542>) or survival curves (Combescure, 2014, <doi:10.1002/sim.6111>) from aggregated data.

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