Work

Flexible inference of optimal individualized treatment strategy in covariate adjusted randomization with multiple covariates

Year: 2023

Type: article

Abstract: To maximize clinical benefit, clinicians routinely tailor treatment to the individual characteristics of each patient, where individualized treatment rules are needed and are of significant research interest to statisticians. In the covariate-adjusted randomization clinical trial with many covariates, we model the treatment effect with an unspecified function of a single index of the covariates and leave the baseline response completely arbitrary. We devise a class of estimators to consistently estimate the treatment effect function and its associated index while bypassing the estimation of the baseline response, which is subject to the curse of dimensionality. We further develop inference tools to identify predictive covariates and isolate effective treatment region. The usefulness of the methods is demonstrated in both simulations and a clinical data example.

Source: Electronic Journal of Statistics

DOI: 10.1214/23-ejs2127

Authors: Trinetri Ghosh, Yanyuan Ma, Rui Song, Pingshou Zhong

Institutions: University of Wisconsin–Madison, Pennsylvania State University, North Carolina State University, University of Illinois Chicago

Language: English

Topic: Advanced Causal Inference Techniques

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $675