Analysis of longitudinal data with binary (time-to-event) or continuous outcomes. Estimates the mean counterfactual outcome or counterfactual survival under static, dynamic and stochastic interventions on treatment (exposure) and monitoring events over time. Adjusts for measured time-varying confounding and informative right-censoring. Possible estimators are: bounded IPW, hazard-based IPW (NPMSM), hazard-based IPW MSM, direct plug-in for longitudinal G-formula (GCOMP), long-format TMLE and infinite-dimensional TMLE (iTMLE). Use data-adaptive estimation with machine learning algorithms implemented in xgboost or h2o (Extreme Gradient Boosting, Random Forest, Deep Neural Nets). Perform model selection with V-fold cross-validation. The exposure, monitoring and censoring variables can be binary, categorical or continuous. Each can be multivariate (e.g., can use more than one column of dummy indicators for different censoring events). The input data needs to be in long format.
|License||MIT + file LICENSE|
|Package repository||View on GitHub|
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