Variable selection in high dimensions for discrete-outcome individualized treatment rules: Reducing severity of depression symptoms

Despite growing interest in estimating individualized treatment rules, little attention has been given the binary outcome setting. Estimation is challenging with nonlinear link functions, especially when variable selection is needed. We use a new computational approach to solve a recently proposed doubly robust regularized estimating equation to accomplish this difficult task in a case study of depression treatment. We demonstrate an application of this new approach in combination with a weighted and penalized estimating equation to this challenging binary outcome setting. We demonstrate the double robustness of the method and its effectiveness for variable selection. The work is motivated by and applied to an analysis of treatment for unipolar depression using a population of patients treated at Kaiser Permanente Washington.

Automated summary

Despite increasing interest in individualized treatment rules, there is little attention given to binary outcomes. This study introduces a new computational approach to estimate a doubly robust regularized equation for binary outcomes in depression treatment. The approach demonstrates its double robustness and effectiveness in variable selection. The study is motivated by and applied to the analysis of treatment for unipolar depression using data from patients treated at Kaiser Permanente Washington.