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 antiretroviral triple vs monotherapy, using data from the AIDS Clinical Trial Group (ACTG) 175 (monotherapy vs dual therapy) and ACTG 320 (dual vs 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 with monotherapy, in individuals with baseline CD4 cell 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.