While randomized controlled trials (RCTs) are critical for establishing the efficacy of new therapies, there are limitations regarding what comparisons can be made directly from trial data. RCTs are limited to a small number of comparator arms and often compare a new therapeutic to a standard of care which has already proven efficacious. It is sometimes of interest to estimate the efficacy of the new therapy relative to a treatment that was not evaluated in the same trial, such as a placebo or an alternative therapy that was evaluated in a different trial. Such multi-study comparisons are challenging because of potential differences between trial populations that can affect the outcome. In this paper, two bridging estimators are considered that allow for comparisons of treatments evaluated in different trials using data fusion methods to account for measured differences in trial populations. A "multi-span'' estimator leverages a shared arm between two trials, while a "single-span'' estimator does not require a shared arm. A diagnostic statistic that compares the outcome in the standardized shared arms is provided. The two estimators are compared in simulations, where both estimators demonstrate minimal empirical bias and nominal confidence interval coverage when the identification assumptions are met. The estimators are applied to data from the AIDS Clinical Trials Group 320 and 388 to compare the efficacy of two-drug versus four-drug antiretroviral therapy on CD4 cell counts among persons with advanced HIV. The single-span approach requires fewer identification assumptions and was more efficient in simulations and the application.

翻译：尽管随机对照试验对于确立新疗法的疗效至关重要，但其直接通过试验数据进行的比较存在局限性。随机对照试验仅包含少量比较组，且通常将新疗法与已证实有效的标准疗法进行对比。有时需要评估新疗法相较于未在同一试验中评估的其他治疗（如安慰剂或在不同试验中评估的替代疗法）的疗效。此类跨研究比较具有挑战性，因为试验人群间的潜在差异可能影响结果。本文考虑了两种桥接估计方法，利用数据融合技术校正试验人群中已测量的差异，从而实现不同试验中治疗效果的比较。“多跨度”估计量利用两个试验间的共享臂，而“单跨度”估计量则无需共享臂。研究还提供了一种诊断统计量，用于比较标准化共享臂中的结果。在模拟中比较了两种估计量，结果表明当识别假设成立时，两种估计量均表现出最小经验偏差和名义置信区间覆盖率。将估计量应用于艾滋病临床试验组320和388的数据，以比较晚期HIV感染者中两药与四药抗逆转录病毒疗法对CD4细胞计数的疗效。单跨度方法所需的识别假设更少，且在模拟和应用中效率更高。