stremr (0.4)

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Streamlined Estimation of Survival for Static, Dynamic and Stochastic Treatment and Monitoring Regimes.

Analysis of longitudinal time-to-event or time-to-failure data. Estimates the counterfactual discrete survival curve under static, dynamic and stochastic interventions on treatment (exposure) and monitoring events over time. Estimators (IPW, MSM-IPW, GCOMP, longitudinal TMLE) adjust for measured time-varying confounding and informative right-censoring. Model fitting can be performed either with GLM or H2O-3 machine learning libraries. The exposure, monitoring and censoring variables can be coded as either 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.

Maintainer: Oleg Sofrygin
Author(s): Oleg Sofrygin [aut, cre], Mark J. van der Laan [aut], Romain Neugebauer [aut]

License: GPL (>= 2)

Uses: assertthat, data.table, pander, R6, Rcpp, rmarkdown, speedglm, stringr, zoo, RUnit, foreach, devtools, doParallel, knitr, magrittr, h2o

Released about 3 years ago.

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