时 间：2024年7月17日10:00 - 11:00
地 点：普陀区理科大楼A1514
报告人：Bo Zhang福瑞德·哈金森癌症研究中心助理教授
主持人：项冬冬 华东师范大学教授
摘 要：
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, HIV Prevention Trials Network Study 084 (HPTN 084), comparing long-acting cabotegravir, a new HIV pre-exposure prophylaxis (PrEP) agent, to the FDA-approved daily oral tenofovir disoproxil fumarate plus emtricitabine (TDF/FTC) in a population of heterosexual women in 7 African countries. One key complication of interpreting study results in an active-controlled trial like HPTN 084 is that the placebo arm is not present 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. \bz{In this article, we study statistical inference for the intention-to-treat (ITT) effect of the active control using relevant historical placebo-controlled trials data under the potential outcomes (PO) framework}. 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 in HPTN 084 using data from an earlier Phase 3, placebo-controlled trial of daily oral TDF/FTC (Partners PrEP).
报告人简介:
Bo Zhang is an assistant professor whose research focuses on developing novel causal inference methods for designing and analyzing retrospective observational studies and clinical trials with noncompliance, and applying them to the health sciences. His specific methodological research interests including statistical matching, randomization inference, instrumental variable methods, precision medicine, and synthesizing evidence from clinical trials and observational studies. He is particularly passionate about applying novel methodologies to analyzing data derived from clinical trials with noncompliance, Medicare and Medicaid claims data, and disease registry database in various clinical settings.