High-Dimensional Inference for Personalized Treatment Decision.

TitleHigh-Dimensional Inference for Personalized Treatment Decision.
Publication TypeJournal Article
Year of Publication2018
AuthorsX Jeng, Jessie, Wenbin Lu, and Huimin Peng
JournalElectron J Stat
Date Published2018

Recent development in statistical methodology for personalized treatment decision has utilized high-dimensional regression to take into account a large number of patients' covariates and described personalized treatment decision through interactions between treatment and covariates. While a subset of interaction terms can be obtained by existing variable selection methods to indicate relevant covariates for making treatment decision, there often lacks statistical interpretation of the results. This paper proposes an asymptotically unbiased estimator based on Lasso solution for the interaction coefficients. We derive the limiting distribution of the estimator when baseline function of the regression model is unknown and possibly misspecified. Confidence intervals and p-values are derived to infer the effects of the patients' covariates in making treatment decision. We confirm the accuracy of the proposed method and its robustness against misspecified function in simulation and apply the method to STAR*D study for major depression disorder.

Alternate JournalElectron J Stat
Original PublicationHigh-dimensional inference for personalized treatment decision.
PubMed ID30416643
PubMed Central IDPMC6226259
Grant ListP01 CA142538 / CA / NCI NIH HHS / United States