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Activity Number: 702
Type: Contributed
Date/Time: Thursday, August 4, 2016 : 10:30 AM to 12:20 PM
Sponsor: Biopharmaceutical Section
Abstract #321331 View Presentation
Title: On Analysis of Longitudinal Clinical Trials with Missing Data Using Reference-Based Imputation
Author(s): Lei Pang* and Guanghan Liu
Companies: Merck and Merck Research Laboratories
Keywords: missing data ; clinical trial ; sensitivity analysis ; longitudinal data

Reference-based imputation (RBI) methods have been proposed as sensitivity analysis for longitudinal clinical trials with missing data. The RBI methods multiply impute the missing data in treatment group based on an imputation model built from the reference (control) group data to yield a conservative treatment effect estimate compared to multiple imputation (MI) under missing at random (MAR) . However, the RBI analysis based on the regular MI approach can be overly conservative because it not only applies discount to treatment effect estimate but also posts penalty on the variance estimate. In this paper, we investigate the statistical properties of RBI methods, and propose approaches to derive accurate variance estimates using both frequentist and Bayesian methods for the RBI analysis. Results from simulation studies and applications to longitudinal clinical trial datasets are presented.

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

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