With the advent of effective pre-exposure prophylaxis agents, active-controlled HIV prevention trials have become a common study design. Nevertheless, estimating absolute efficacy relative to a placebo remains important. In this paper, we introduce a novel application of proximal causal inference methods to estimate the counterfactual cumulative HIV incidence under placebo for participants in an active-controlled trial of cabotegravir, using external control data from a placebo-controlled trial with similar eligibility criteria. We leverage baseline sexually transmitted infection status and geographic region as negative control outcome and exposure variables, respectively. We address two key challenges: unmeasured differences in HIV risk between trials and statistical difficulties arising from low HIV incidence rates in both studies. To overcome these challenges, we develop two proximal inference approaches: (1) a semiparametric inverse probability of censoring weighting estimator, and (2) a two-stage regression-based strategy tailored to low-event-rate settings. Our theoretical and numerical investigations demonstrate these methods yield reliable estimates of the counterfactual one-year cumulative HIV incidence under placebo, and provide robust evidence of the superior efficacy of cabotegravir compared with placebo. These findings highlight the potential of proximal inference methods to estimate placebo-controlled effects in both single-arm and active-controlled trials by leveraging external controls.

翻译：随着有效暴露前预防药物的出现，主动对照的HIV预防试验已成为一种常见的研究设计。然而，相对于安慰剂估计绝对疗效仍然至关重要。本文提出了一种近端因果推断方法的新应用，旨在利用一项符合相似入选标准的安慰剂对照试验的外部对照数据，估计卡博特韦主动对照试验参与者在安慰剂条件下的反事实累积HIV发病率。我们分别将基线性传播感染状况和地理区域作为阴性对照结果变量和暴露变量。我们解决了两个关键挑战：试验间HIV风险未测量的差异，以及两项研究中低HIV发病率带来的统计困难。为克服这些挑战，我们开发了两种近端推断方法：(1) 一种半参数逆删失概率加权估计器，以及(2) 一种针对低事件率场景定制的两阶段回归策略。我们的理论和数值研究表明，这些方法能够可靠地估计安慰剂条件下反事实的一年累积HIV发病率，并为卡博特韦相较于安慰剂的优越疗效提供了有力证据。这些发现凸显了近端推断方法在利用外部对照估计单臂试验和主动对照试验中安慰剂对照效应的潜力。