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Abstract:

Bayesian statistical approaches to mixed treatment comparisons (MTCs) are becoming more popular due to their flexibility and interpretability. Many randomized clinical trials report multiple outcomes with possible inherent correlations. Moreover, MTC data are typically sparse and researchers often choose study arms based on the outcomes of previous trials. In this talk, we summarize existing hierarchical Bayesian methods for MTCs with a single outcome, and subsequently introduce novel Bayesian approaches for multiple outcomes simultaneously, rather than in separate MTC analyses. We do this by incorporating missing data and correlation structure between outcomes through contrast- and arm-based parameterizations that consider any unobserved treatment arms as missing data to be imputed. We also extend the model to apply to all types of generalized linear model outcomes, such as count or continuous responses. We develop a new measure of inconsistency under our missing data framework, having more straightforward interpretation and implementation than standard methods. We off

er a simulation study under various missingness mechanisms (e.g., MCAR, MAR, and MNAR) providing evidence that our models outperform existing models in terms of bias and MSE, then illustrate our methods with two real MTC datasets. We close with a discussion of our results and a few avenues for future methodological development.
This talk represents joint work with Hwanhee Hong and Haitao Chu of the University of Minnesota.

BIO:

Brad Carlin is Mayo Professor in Public Health and Professor and Head of the
Division of Biostatistics at the University of Minnesota. He has published more than 135 papers in refereed books and journals, and supervised or co-supervised 19 PhD dissertations to completion.
He is co-author of three popular textbooks, 鈥淏ayesian Methods for Data Analysis鈥� with Tom Louis, 鈥淗ierarchical Modeling and Analysis for Spatial Data鈥� with Sudipto Banerjee and Alan Gelfand, and "Bayesian Adaptive Methods for Clinical Trials" with Scott Berry, J. Jack Lee, and Peter Muller.聽 He is a winner of the Mortimer Spiegelman Award from the APHA, and from 2006-2009 served as editor-in-chief of Bayesian Analysis, the official journal of the International Society for Bayesian Analysis (ISBA).聽 Prof. Carlin has extensive experience teaching short courses and tutorials, and has won teaching awards from both the faculty and the graduate students at the University of Minnesota, as well as from the Joint Statistical Meetings CE program. During his spare time, Brad is a musician and bandleader, providing keyboards and vocals in a variety of venues, some of the more interesting of which are visible by typing the phrase "Bayesian cabaret" into the search window at YouTube.