Proximal indirect comparison

TL;DR

This paper introduces a new method for indirect treatment comparison using proxies to address unobserved effect modifiers, enabling more reliable estimates when direct data is unavailable.

Contribution

It provides a novel proximal identification framework with doubly-robust estimators for indirect comparisons in randomized trials.

Findings

Successfully applied to weight loss trials demonstrating the method's practical utility.

Achieved asymptotic normality under mild conditions, ensuring reliable inference.

Enhanced robustness against model misspecification through doubly-robust estimation.

Abstract

We consider the problem of indirect comparison, where a treatment arm of interest is absent by design in one randomized controlled trial but available in the other. The former is the target trial, and the latter is the source trial. The identifiability of the target population average treatment effect often relies on conditional transportability assumptions. However, it is a common concern whether all relevant effect modifiers are measured and controlled for. We give a new proximal identification result in the presence of shifted, unobserved effect modifiers based on proxies: an adjustment proxy in both trials and an additional reweighting proxy in the source trial. We propose an estimator which is doubly-robust against misspecifications of the so-called bridge functions and asymptotically normal under mild consistency of estimators for the bridge functions. We use two weight management trials as a context to illustrate selection of proxies and apply our method to compare the weight loss effect of active treatments from these trials.