A RELUCTANT ADDITIVE MODEL FRAMEWORK FOR INTERPRETABLE NONLINEAR INDIVIDUALIZED TREATMENT RULES

• 成果类型: Article
• 署名作者: Maronge, Jacob M.; Huling, Jared D.; Chen, Guanhua
• 署名单位: University of Texas System; UTMD Anderson Cancer Center; University of Minnesota System; University of Minnesota Twin Cities; University of Wisconsin System; University of Wisconsin Madison
• 刊物名称: ANNALS OF APPLIED STATISTICS
• ISSN/ISSBN: 1932-6157
• DOI: 10.1214/23-AOAS1767
• 发表日期: 2023
• 页码: 3384-3402
• 关键词: subgroup identification; CONCORDANCE; selection

摘要

Individualized treatment rules (ITRs) for treatment recommendation is an important topic for precision medicine as not all beneficial treatments work well for all individuals. Interpretability is a desirable property of ITRs, as it helps practitioners make sense of treatment decisions, yet there is a need for ITRs to be flexible to effectively model complex biomedical data for treat-ment decision making. Many ITR approaches either focus on linear ITRs, which may perform poorly when true optimal ITRs are nonlinear, or black -box nonlinear ITRs, which may be hard to interpret and can be overly com-plex. This dilemma indicates a tension between interpretability and accuracy of treatment decisions. Here we propose an additive model-based nonlinear ITR learning method that balances interpretability and flexibility of the ITR. Our approach aims to strike this balance by allowing both linear and nonlinear terms of the covariates in the final ITR. Our approach is parsimonious in that the nonlinear term is included in the final ITR only when it substantially im-proves the ITR performance. To prevent overfitting, we combine crossfitting and a specialized information criterion for model selection. Through exten-sive simulations we show that our methods are data-adaptive to the degree of nonlinearity and can favorably balance ITR interpretability and flexibil-ity. We further demonstrate the robust performance of our methods with an application to a cancer drug sensitive study.