TITLE: Enhancing Treatment Effect Assessment through Covariate Adjustment Methods
SPEAKERS: Margaret Gamalo, Jinma Ren, Wenjin Wang, Pfizer Inc
MODERATOR: Li-An Xu
Assessing treatment effects in clinical trials frequently encounters bias due to unbalanced baseline characteristics. Absent use of appropriate statistical techniques or comprehensive measurement of potential prognostic factors can introduce biases in various scenarios. These include comparing single-arm trials with external controls, evaluating effects in observational studies, or even within randomized clinical trials (RCTs), and investigating treatment effects in subgroup analyses.
Effective mitigation of such biases and improvement of accuracy and test power necessitates adjusting for baseline prognostic factors. The literature offers a range of covariate adjustment methods. In this informative tutorial session, we embark on a comprehensive journey:
- We will commence by reviewing guidelines issued by regulatory authorities and health technology assessment bodies. Understanding the regulatory landscape and insight is crucial for robust analyses.
- Subsequently, we will delve into prevalent covariate adjustment methods. This will encompass:
- Techniques leveraging individual participant data (IPD), such as propensity- score-based weighting, g-computation, and doubly robust estimators.
- Approaches applicable when only partial trial IPD and summary data fromexternal sources are available, namely simulated treatment comparison (STC)and matching-adjusted indirect comparison (MAIC).
- Exploring Bayesian strategies for incorporating external controls.
- Throughout the session, we will substantiate concepts through comparative simulations, offering a hands-on understanding of the presented methods’ strengths and limitations.
- To bring theory to life, we will illustrate an application involving the assessment of differential placebo response proportions between two pivotal clinical trials.
Join us for an illuminating tutorial that equips you with invaluable insights into the world of covariate adjustment methods, ensuring robust and accurate treatment effect assessment in clinical trials.
Margaret (Meg) Gamalo, PhD is Statistics Head for Inflammation and Immunology in Pfizer Global Product Development. She combines expertise in biostatistics, regulatory science and adult and pediatric clinical development. Prior to joining Pfizer, she was Research Advisor, Global Statistical Sciences at Eli Lilly and Company and as Mathematical Statistician at the Food and Drug Administration. Meg leads the Complex Innovative Design Task Force at the Biotechnology Innovation Organization. She also actively contributes to research topics within the European Forum for Good Clinical Practice – Children’s Medicine Working Party. Meg is currently Editor-in-Chief of the Journal of Biopharmaceutical Statistics and is actively involved in many statistical activities in the American Statistical Association. Recently, she was elected Fellow of the American Statistical Association.
Jinma Ren is a director of biostatistics in the Statistical Research & Data Science Center (SRDC) and its Health Economics & Outcomes Research (HEOR) Statistics Group at Pfizer Inc., where he has provided statistical support to oncology and other areas on HEOR projects, mainly for health technology assessment (HTA). Currently, as statistics lead, he is helping design and analyze externally controlled studies to support HTA submissions for Elranatamab. Jinma is also leading the analyses of patient-reported outcomes (PRO) in Paxlovid trials. Prior to join Pfizer, Jinma has 15+ years of outcomes research experience from both academia and industry, and he serves on the editorial board of two major peer-reviewed journals. Jinma holds both PhD degree in epidemiology and health statistics and MD degree in preventive medicine.
Wenjin Wang, PhD is a senior director in Global Biometrics & Data Management within Pfizer Research & Development division, Pfizer Inc. Currently he is leading the statistical group for the development of gastrointestinal therapeutic products. With over two decades of experience in the pharmaceutical industry, Wenjin has played a pivotal role in driving the success of developing several products that are in the market today by merging statistical expertise and regulatory insight. Wenjin is also actively serving in professional activities. He is a co-founder and esteemed board member of the International Society for Biopharmaceutical Statistics. He serves executive and program committee roles for the Annual Deming Conference on Applied Statistics since 2005. He is a book review editor for Journal of Biopharmaceutical Statistics. He was a ASA president appointed member of the inauguration committee for the ASA Conference on Statistical Practice and contributed to the program organization over four years. He was also a representative of the Biotechnology Industry Organization (BIO) Adaptive Design Working Group.