Adjustment Set Selection for Estimating Optimal Treatment Rules under Confounding
There is an increasing call for individualized treatment rules, which leverage individual patient characteristics to recommend treatments or interventions, tailoring recommendations based on their covariates. This is particularly of interest for the care of conditions such as depression, for which many treatment options are available with similar average effectiveness but with large heterogeneity in individual responses. In parallel, there has been a growing interest in machine learning methods for causal inference and variable selection. We compared several strategies for variable adjustment in a dynamic marginal structural modeling approach to estimating an optimal individualized treatment rule and investigated the performance of Outcome Adaptive Lasso, Group Lasso and Doubly Robust Estimation, Double-index Propensity Score, the High-Dimensional Balancing Propensity Score, and the Causal Ball Lasso as variable selection methods for the propensity score. Our results demonstrate that these all provided similar unbiased estimates. However, methods differed in their ability to exclude extraneous variables and in computational burden. We found statistical efficiency is gained when variable selection approaches were for the propensity score were used and by including variables in the outcome model. We applied all methods to determine the optimal treatment rule, treating with either selective serotonin reuptake inhibitors or serotonin and norepinephrine reuptake inhibitors, for unipolar depression in individuals aged 13 years and older. This analysis, which used electronic health records from 74,058 Kaiser Permanente Washington patients with a new antidepressant dispensing between 2008 and 2018, suggested tailoring treatment based on baseline symptom severity did not impact symptom severity 6 months later.