Analysis of longitudinal data with binary (timetoevent) 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 timevarying confounding and informative rightcensoring. Possible estimators are: bounded IPW, hazardbased IPW (NPMSM), hazardbased IPW MSM, direct plugin for longitudinal Gformula (GCOMP), longformat TMLE and infinitedimensional TMLE (iTMLE). Use dataadaptive estimation with machine learning algorithms implemented in xgboost or h2o (Extreme Gradient Boosting, Random Forest, Deep Neural Nets). Perform model selection with Vfold crossvalidation. 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.
Package details 


Maintainer  
License  MIT + file LICENSE 
Version  0.8.99 
URL  https://github.com/osofr/stremr 
Package repository  View on GitHub 
Installation 
Install the latest version of this package by entering the following in R:

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