Biostatistics Weekly Seminar

Sensitivity analyses for unobserved effect moderation when generalizing from trial to population

Elizabeth Stuart, PhD

Associate Dean for Education, Professor
Depts of Mental Health, Biostatistics, and Health Policy and Management
Johns Hopkins Bloomberg School of Public Health

In the presence of treatment effect heterogeneity, the average treatment effect (ATE) in a randomized controlled trial (RCT) may differ from the average effect of the same treatment if applied to a target population of interest. But for policy purposes we may desire an estimate of the target population ATE. If all treatment effect moderators are observed in the RCT and in a dataset representing the target population, then we can obtain an estimate for the target population ATE by adjusting for the difference in the distribution of the moderators between the two samples. However, that is often an unrealistic assumption in practice. This talk will discuss methods for generalizing treatment effects under that assumption, as well as sensitivity analyses for when we cannot adjust for a specific moderator observed in the RCT because we do not observe it in the target population. Outcome-model and weighting-based sensitivity analysis methods are presented. The methods are applied to examples in drug abuse treatment. Implications for study design and analyses are also discussed, when interest is in a target population ATE.

MRBIII, Room 1220
17 October 2018

Speaker Itinerary

Topic revision: r4 - 03 Oct 2018, ThomasStewart

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