Date: 2020-03-20

Time: 15:30-16:30

Location: BURNSIDE 1205

Abstract:

Historical data from previous studies may be utilized to strengthen statistical inference. Under the Bayesian framework incorporation of information obtained from any source other than the current data is facilitated through construction of an informative prior. The existing methodology for defining an informative prior based on historical data relies on measuring similarity to the current data at the study level that can result in discarding useful individual patient data (IPD). In this talk I present a family of priors that utilize IPD to strengthen statistical inference. IPD-based priors can be obtained as a weighted likelihood of the historical data where each individual’s weight is a function of their distance to the current study population. It is demonstrated that the proposed prior construction approach can considerably improve estimation accuracy and precision in compare with existing methods.

Speaker

Shirin Golchi is an Assistant Professor of Biostatistics, in the Department of Epidemiology, Biostatistics, and Occupational Health, at McGill University. Her research program is generally devoted to Bayesian modelling, Bayesian adaptive clinical trials, Computational statistics. She has worked on a variety of problems with applications in health sciences, physics, social sciences and mathematical biology.