Eloise Kaizar, Ohio State University
Randomized controlled trials are often thought to provide definitive evidence on the magnitude of treatment effects. But because treatment modifiers may have a different distribution in a real world population than among trial participants, trial results may not directly reflect the average treatment effect that would follow real world adoption of a new treatment. Recently, weight-based methods have been repurposed to more provide more relevant average effect estimates for real populations. In this talk, I summarize important analytical choices involving what should and should not be borrowed from other applications of weight-based estimators, make evidence-based recommendations about confidence interval construction, and present conjectures about best choices for other aspects of statistical inference.
Eloise Kaizar is Associate Professor of Statistics at The Ohio State University. Her primary research focus is on assessing the effects and safety of medical exposures and interventions, especially those whose effects are heterogeneous across populations or measured with rare event outcomes. As such, she has worked on methodology to combine multiple sources of information relevant to the same broad policy or patient-centered question. She is particularly interested in how data collected via different study designs can contribute complementary information.