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.

翻译：对治疗、干预或暴露的比较是流行病学研究的核心关注点，但出于实践或伦理原因，直接比较并不总是可行。本文详细阐述了一种跨研究融合方法，用于比较不同治疗方案。研究实例基于艾滋病临床试验组（ACTG）175（单药与双药治疗）和ACTG 320（双药与三药治疗）的数据，旨在比较HIV感染者接受三药联合抗逆转录病毒治疗与单药治疗时，死亡、艾滋病或CD4细胞计数下降超过50%这一复合结局的风险。我们回顾了一组识别假设，并采用逆概率加权估计量来评估风险差异，该估计量利用了共享试验臂（双药治疗）。我们提出了一种基于共享臂比较的融合诊断方法，用于识别假设违背的潜在信号。将数据融合估计量和诊断方法应用于ACTG试验后，结果显示：对于基线CD4计数介于50至300 cells/mm$^3$的个体，三药治疗相比单药治疗可降低风险。桥接治疗比较能够解决单一数据源无法独立回答的问题，但基于融合的有效推断需审慎考虑基础假设。