Proximal Learning for Trials With External Controls: A Case Study in HIV Prevention

TL;DR

This paper applies proximal causal inference methods to estimate the placebo effect in HIV prevention trials using external control data, addressing unmeasured confounding and low incidence challenges.

Contribution

It introduces two novel proximal inference approaches for estimating counterfactual outcomes in active-controlled trials with external controls.

Findings

Reliable estimates of placebo HIV incidence achieved

Demonstrated superior efficacy of cabotegravir over placebo

Methods applicable to low-incidence and single-arm trials

Abstract

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.