ITRLearn-package: Statistical Learing for Individualized Treatment Regime

In ITRLearn: Statistical Learning for Individualized Treatment Regime

Description

Maximin-projection learning (MPL, Shi, et al., 2018) is implemented for recommending a meaningful and reliable individualized treatment regime for future groups of patients based on the observed data from different populations with heterogeneity in individualized decision making. Q-learning and A-learning are implemented for estimating the groupwise contrast function that shares the same marginal treatment effects. The packages contains classical Q-learning and A-learning algorithms for a single stage study as a byproduct.

Details

Package:	ITRLearn
Type:	Package
Version:	1.0-1
Date:	2018-11-14
License:	GPL-2

Author(s)

Chengchun Shi, Rui Song, Wenbin Lu and Bo Fu

Maintainer: Chengchun Shi <[e-mail]>

References

Shi, C., Song, R., Lu, W., and Fu, B. (2018). Maximin Projection Learning for Optimal Treatment Decision with Heterogeneous Individualized Treatment Effects. Journal of the Royal Statistical Society, Series B, 80: 681-702.

ITRLearn documentation built on May 2, 2019, 11:03 a.m.