Elio-z/MOSS-ATE: One-step TMLE for survival analysis (ATE Version with prediction method)

Estimate counterfactual survival curve under static or dynamic interventions on treatment (exposure), while at the same time adjust for measured counfounding. Targeted Maximum Likelihood Estimate (TMLE) approach is employed to create a doubly robust and semi-parametrically efficient estimator. Machine Learning algorithms (SuperLearner) are implemented to all stages of the estimation.

Getting started

Package details

AuthorWilson,Jie
Maintainer
LicenseGPL-2
Version0.4.0
Package repositoryView on GitHub
Installation Install the latest version of this package by entering the following in R:
install.packages("devtools")
library(devtools)
install_github("Elio-z/MOSS-ATE")
Elio-z/MOSS-ATE documentation built on Jan. 28, 2019, 12:21 a.m.