Variable selection for individualised treatment rules with discrete outcomes

• Published in: Journal of the Royal Statistical Society Series C (Applied Statistics) Vol. 73; no. 2; pp. 298 - 313
• Main Authors: Bian, Zeyu, Moodie, Erica E M, Shortreed, Susan M, Lambert, Sylvie D, Bhatnagar, Sahir
• Format: Journal Article
• Language: English
• Published: US Oxford University Press 01.03.2024
• Subjects: Data collection; Decision making; Health status; Observational studies; Original; Stress management; Variables; penalisation; precision medicine; double robustness; variable selection; weighted generalised linear model
• ISSN: 0035-9254; 1467-9876; 1467-9876
• DOI: 10.1093/jrsssc/qlad096

Abstract An individualised treatment rule (ITR) is a decision rule that aims to improve individuals’ health outcomes by recommending treatments according to subject-specific information. In observational studies, collected data may contain many variables that are irrelevant to treatment decisions. Including all variables in an ITR could yield low efficiency and a complicated treatment rule that is difficult to implement. Thus, selecting variables to improve the treatment rule is crucial. We propose a doubly robust variable selection method for ITRs, and show that it compares favourably with competing approaches. We illustrate the proposed method on data from an adaptive, web-based stress management tool.