Title:
Generalizing the intention-to-treat effect of an active control from historical placebo-controlled trials to an active-controlled trial
Abstract:
In many clinical settings, an active-controlled trial design (e.g., a non-inferiority or superiority design) is often used to compare an experimental medicine to an active control (e.g., an FDA-approved, standard therapy). One prominent example is a recent phase 3 efficacy trial comparing long-acting cabotegravir, a new HIV pre-exposure prophylaxis (PrEP), to the FDA-approved daily oral tenofovir diphosphate plus emtricitabine (TDF/FTC). One key complication in an active-controlled trial is that the placebo arm is lost, and the efficacy of the active control (and hence the experimental drug) compared to the placebo can only be inferred by leveraging other data sources. In this article, we propose a rigorous causal inference framework to infer the intention-to-treat (ITT) effect of the active control using relevant historical placebo-controlled trial data of the active control. We highlight the role of adherence and unmeasured confounding, discuss in detail identification assumptions and two modes of inference (point versus partial identification), propose estimators under identification assumptions permitting point identification, and lay out sensitivity analyses needed to relax identification assumptions. We applied our framework to estimating the intention-to-treat effect of daily oral TDF/FTC versus placebo using data from an active-controlled trial (HPTN 084) and an earlier Phase 3, placebo-controlled trial of daily oral TDF/FTC (Partners PrEP). This is joint work with Qijia He, Fei Gao, Oliver Dukes and Sinead Delany-Moretlwe.
Affiliation:
Bo Zhang, PhD
Assistant Professor, Biostatistics, Bioinformatics and Epidemiology Program, Vaccine and Infectious Disease Division, Fred Hutch
