Department of Mathematics - Seminar on Statistics - Modern approaches for evaluating treatment effect heterogeneity from clinical and observational data
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This talk reviews statistical methods for evaluating heterogeneous treatment effects (HTE) from randomized clinical trials and observational data including subgroup identification and estimation of individualized treatment regimens. We use typology of methods proposed in Lipkovich, Dmitrienko and D’Agostino (2017) and discuss their advantages and disadvantages. A simulated data set is used to illustrate challenges of estimating HTEs.
Ilya Lipkovich is a Sr. Research Advisor at Eli Lilly and Company. He received his Ph.D. in Statistics from Virginia Tech in 2002 and has 20 years of statistical consulting experience in pharmaceutical industry. He is an ASA Fellow and published on subgroup identification in clinical data, analysis with missing data, and causal inference. He is a frequent presenter at conferences, a co-developer of subgroup identification methods, and a co-author of the books “Analyzing Longitudinal Clinical Trial Data. A Practical Guide” and “Estimands, Estimators and Sensitivity Analysis in Clinical Trials.”