Model selection for survival individualized treatment rules using the jackknife estimator.

ABSTRACT:

Background

Precision medicine is an emerging field that involves the selection of treatments based on patients' individual prognostic data. It is formalized through the identification of individualized treatment rules (ITRs) that maximize a clinical outcome. When the type of outcome is time-to-event, the correct handling of censoring is crucial for estimating reliable optimal ITRs.

Methods

We propose a jackknife estimator of the value function to allow for right-censored data for a binary treatment. The jackknife estimator or leave-one-out-cross-validation approach can be used to estimate the value function and select optimal ITRs using existing machine learning methods. We address the issue of censoring in survival data by introducing an inverse probability of censoring wei

SUBMITTER: Honvoh GD 

PROVIDER: S-EPMC9773469 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature