Topics in Causal Inference for Individualized Treatment

Abstract

Optimal treatment rules, which use patient characteristics to tailor treatment decisions, are a promising way to improve outcomes when there is patient-to-patient variability in treatment effect. While investigators can best evaluate a treatment rule through a randomized trial of the rule against the standard of care, they must first find the best candidate rule or rules using other data. Causal inference methods enable leveraging electronic health records and standard clinical trial data to identify optimal rules and estimate their value. In this dissertation, we advance causal inference methodology for treatment rules. Our primary application is the treatment of major depression. In Chapter 1, we introduce individual treatment rules and our application of major depression treatment. In Chapter 2, we provide a method that uses summary statistics widely available in published clinical trial results to bound the benefit of optimally assigning treatment to each patient. In Chapter 3, we propose a method to estimate the value of treatment rules that optimize a primary outcome under the constraint that a risk outcome is below a specified threshold. In Chapter 4, we provide our constrained treatment rule value estimator with two alternative confidence intervals, a bootstrap confidence interval with improved finite sample performance, and an analytical confidence interval with improved theoretical guarantees.