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.
