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Activity Number: 438 - Missing Data Issues in Public Health Studies and Survey Sampling in the Era of Data Science
Type: Invited
Date/Time: Wednesday, July 31, 2019 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistics in Epidemiology
Abstract #300311 Presentation
Title: New Predictive Mean Matching Imputation Methods for Cluster Randomized Trials
Author(s): Brittney Bailey* and Rebecca Andridge
Companies: Amherst College and The Ohio State University College of Public Health
Keywords: missing data; multiple imputation; predictive mean matching; cluster randomized trial; clinical trial

Random effects regression imputation has been recommended for multiple imputation in cluster randomized trials since it is congenial to analyses that use random effects regression. However, this method relies heavily on model assumptions and may not be the most powerful or most appropriate analysis, particularly when there are few clusters. We propose three new multiple imputation procedures based on predictive mean matching (PMM) that are more robust to misspecification of the imputation model. Instead of using a single model to define “closeness” of predictive means, we use two models in combination: one that ignores clustering (PMM-IGN) and one that uses fixed effects for clusters (PMM-FE). On their own, these imputation models result in underestimation (PMM-IGN) or overestimation (PMM-FE) of variance estimates. To leverage the ambidirectional bias, our proposed PMM procedures combine these two models (1) using a weighted distance metric; (2) using a weighted average of the responses selected for imputation; or (3) using a weighted draw from the selected responses. Our methods effectively reduce the bias in the variance estimates relative to established methods.

Authors who are presenting talks have a * after their name.

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