Public Health Seminar Series:

Bayesian Non-Inferiority Trial Design in Comparative Effectiveness Research for Continuous Outcome by Samiran Ghosh, PhD

Friday, November 6, 2015 12:00 PM - 1:00 PM Calit2 Auditorium OCW Video Archive
Seminar Abstract

Randomized controlled trials (RCTs) are an indispensable source of information about efficacy of treatments in almost any disease area. With the availability of multiple treatment options, comparative effectiveness research (CER) is gaining importance for better and informed health care decisions. However design and analysis of effectiveness trial is much more complex than the efficacy trial. The effect of including an active comparator arm in a RCT is immense. This gives rise to superiority and non-inferiority trials. The non-inferiority (NI) RCT design plays a fundamental role in CER which will be focus of this talk. In the past decade many statistical methods have been developed, though largely in the frequentist setup. However, availability of historical placebo-controlled trial is useful and if integrated in the current NI trial design, can provide better precision for CER. Bayesian paradigm provides a natural path to integrate historical as well as current trial data via sequential learning in the NI setup. In this talk we will discuss both fraction margin and fixed margin based Bayesian approach for three-arm trial with continuous outcome. We will also discuss some interesting open problems related to NI trial in the RCT framework.

Speaker Biography - Samiran Ghosh, PhD

Samiran Ghosh, PhD
Samiran Ghosh, PhD Assistant Professor, Molecular Medicine & Genetics, Dept. of Family Medicine & Public Health Sciences Wayne State University
Dr. Ghosh earned his doctorate degree in statistics at the University of Connecticut. Before joining Wayne State University, he held appointments at Indiana University and Weill Cornell Medical College in New York. He currently co-directs the NIEHS-funded Integrative Health Sciences Facility Core (CURES) at Wayne State. Dr. Ghosh's methodological research focuses on survival analysis, generalized linear model, multilevel model, model selection in high dimensional data, machine learning and Bayesian paradigm. He is also interested in design and analysis of clinical trials. Especially issues related to missing data, sample size and bias in longitudinal clinical trial are his recent research focus. On the application side his major focus is on health services, outcomes research and mental health.

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