Bridged treatment comparisons: an illustrative application in HIV treatment

TL;DR

This paper introduces a fusion approach for comparing treatments across different studies, exemplified by HIV therapy trials, enabling indirect comparisons when direct head-to-head trials are unavailable.

Contribution

It presents a novel data fusion methodology with diagnostic tools for valid treatment comparisons across separate studies, demonstrated through HIV treatment data.

Findings

Triple therapy reduces risk compared to monotherapy in certain patients.

The fusion estimator provides consistent treatment effect estimates under specific assumptions.

Diagnostic methods can identify violations of assumptions in data fusion analyses.

Abstract

Comparisons of treatments, interventions, or exposures are of central interest in epidemiology, but direct comparisons are not always possible due to practical or ethical reasons. Here, we detail a fusion approach to compare treatments across studies. The motivating example entails comparing the risk of the composite outcome of death, AIDS, or greater than a 50% CD4 cell count decline in people with HIV when assigned triple versus mono antiretroviral therapy, using data from the AIDS Clinical Trial Group (ACTG) 175 (mono versus dual therapy) and ACTG 320 (dual versus triple therapy). We review a set of identification assumptions and estimate the risk difference using an inverse probability weighting estimator that leverages the shared trial arms (dual therapy). A fusion diagnostic based on comparing the shared arms is proposed that may indicate violation of the identification assumptions. Application of the data fusion estimator and diagnostic to the ACTG trials indicates triple therapy results in a reduction in risk compared to monotherapy in individuals with baseline CD4 counts between 50 and 300 cells/mm$^3$. Bridged treatment comparisons address questions that none of the constituent data sources could address alone, but valid fusion-based inference requires careful consideration of the underlying assumptions.