Identification of the optimal treatment regimen in the presence of missing covariates.

ABSTRACT: Covariates associated with treatment-effect heterogeneity can potentially be used to make personalized treatment recommendations towards best clinical outcomes. Methods for treatment-selection rule development that directly maximize treatment-selection benefits have attracted much interest in recent years, due to the robustness of these methods to outcome modeling. In practice, the task of treatment-selection rule development can be further complicated by missingness in data. Here, we consider the identification of optimal treatment-selection rules for a binary disease outcome when measurements of an important covariate from study participants are partly missing. Under the missing at random assumption, we develop a robust estimator of treatment-selection rules under the direct-optimization

SUBMITTER: Huang Y 

PROVIDER: S-EPMC6954309 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature