Robust regression for optimal individualized treatment rules.

TitleRobust regression for optimal individualized treatment rules.
Publication TypeJournal Article
Year of Publication2019
AuthorsXiao, W, H H. Zhang, and W Lu
JournalStat Med
Date Published2019 May 20
KeywordsAlgorithms, Clinical Protocols, Humans, Models, Statistical, Precision Medicine, Regression Analysis

Because different patients may respond quite differently to the same drug or treatment, there is an increasing interest in discovering individualized treatment rules. In particular, there is an emerging need to find optimal individualized treatment rules, which would lead to the "best" clinical outcome. In this paper, we propose a new class of loss functions and estimators based on robust regression to estimate the optimal individualized treatment rules. Compared to existing estimation methods in the literature, the new estimators are novel and advantageous in the following aspects. First, they are robust against skewed, heterogeneous, heavy-tailed errors or outliers in data. Second, they are robust against a misspecification of the baseline function. Third, under some general situations, the new estimator coupled with the pinball loss approximately maximizes the outcome's conditional quantile instead of the conditional mean, which leads to a more robust optimal individualized treatment rule than the traditional mean-based estimators. Consistency and asymptotic normality of the proposed estimators are established. Their empirical performance is demonstrated via extensive simulation studies and an analysis of an AIDS data set.

Alternate JournalStat Med
Original PublicationRobust regression for optimal individualized treatment rules.
PubMed ID30740747
PubMed Central IDPMC6449186
Grant ListP01 CA142538 / CA / NCI NIH HHS / United States