Doubly robust machine learning for an instrumental variable study of surgical care for cholecystitis

TL;DR

This paper introduces doubly robust machine learning methods for instrumental variable analysis in medical treatment studies, enabling flexible, bias-resistant estimation of treatment effects and heterogeneity, demonstrated through cholecystitis treatment data.

Contribution

It develops novel doubly robust machine learning estimators for IV analysis, allowing flexible nuisance parameter estimation and providing valid inference for causal effects and heterogeneity.

Findings

Operative care generally more effective for cholecystitis.

Surgery benefits vary across patient subgroups.

Flexible methods outperform standard IV approaches in simulations.

Abstract

Comparative effectiveness research frequently employs the instrumental variable design since randomized trials can be infeasible for many reasons. In this study, we investigate and compare treatments for emergency cholecystitis -- inflammation of the gallbladder. A standard treatment for cholecystitis is surgical removal of the gallbladder, while alternative non-surgical treatments include managed care and pharmaceutical options. As randomized trials are judged to violate the principle of equipoise, we consider an instrument for operative care: the surgeon's tendency to operate. Standard instrumental variable estimation methods, however, often rely on parametric models that are prone to bias from model misspecification. We outline instrumental variable estimation methods based on the doubly robust machine learning framework. These methods enable us to employ various machine learning techniques for nuisance parameter estimation and deliver consistent estimates and fast rates of convergence for valid inference. We use these methods to estimate the primary target causal estimand in an IV design. Additionally, we expand these methods to develop estimators for heterogeneous causal effects, profiling principal strata, and a sensitivity analyses for a key instrumental variable assumption. We conduct a simulation study to demonstrate scenarios where more flexible estimation methods outperform standard methods. Our findings indicate that operative care is generally more effective for cholecystitis patients, although the benefits of surgery can be less pronounced for key patient subgroups.