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Activity Number: 337 - Approaches for Modeling Clustered and Longitudinal Data
Type: Contributed
Date/Time: Wednesday, August 5, 2020 : 10:00 AM to 2:00 PM
Sponsor: Biometrics Section
Abstract #313184
Title: Statistical Methods for Dealing with Missing Data in Longitudinal Studies
Author(s): Panpan Zhang* and Sharon Xiangwen Xie
Companies: University of Pennsylvania and University of Pennsylvania
Keywords: Longitudinal analysis; Missing data; Multiple imputation

Missing data is common in longitudinal analysis, rendering it a burgeoning research area in the community. In this presentation, we discuss and compare prominent methods for handling missing data in longitudinal analysis, for instance, multiple imputation. More specifically, we evaluate the performances of the considered methods through bias and efficiency gains under different classes of model settings. We present our results via extensive simulation studies and a real data set related to neurodegenerative diseases.

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

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