Skip to content
master
Switch branches/tags
Code

Latest commit

 

Git stats

Files

Permalink
Failed to load latest commit information.

R and Python codes for estimating heterogeneous survival treatment effect via Machine/Deep Learning methods in observational studies.

We considered 7 machine/deep learning methods designed for right-censored survival outcomes, and contributed a series of simulations to evaluate the operating characteristics of these methods for estimating the treatment effect heterogeneity. A case study examining the heterogeneous treatment effects of two radiotherapy approaches for treating high-risk prostate cancer demonstrates the methods.

Accompanying article is forthcoming. The preprint is available at https://arxiv.org/abs/2008.07044

About

R and Python codes for estimating heterogeneous survival treatment effect via Machine/Deep Learning methods in observational studies.

Resources

Releases

No releases published

Packages

No packages published