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Activity Number: 17
Type: Topic Contributed
Date/Time: Sunday, July 31, 2016 : 2:00 PM to 3:50 PM
Sponsor: Mental Health Statistics Section
Abstract #319472
Title: Reference-Based Imputation Versus Dropout = Failure Imputation for Tackling Missing Data
Author(s): Devan V. Mehrotra* and Fang Liu
Companies: Merck and Merck
Keywords: control-based imputation ; dropout ; estimand ; missing data ; missing at random ; quantile regression

In a typical two-arm (test, reference) randomized clinical trial, the endpoint of interest (e.g., change from baseline in HAMD-17 at 6 weeks) is not observed for dropouts. The resulting missing data problem is commonly tackled by invoking a missing at random (MAR) assumption and proceeding with a mixed model repeated measures (MMRM) analysis. If the MAR assumption is incorrect (it usually is), the estimated between-treatment difference in endpoint means can be notably biased for the estimand of interest. We will discuss bias-reducing methods in which the implicitly imputed mean for test-arm dropouts in the MMRM analysis is explicitly replaced with the estimated mean for either all reference-arm patients or reference-arm dropouts only. The so-called jump-to-reference (J2R) method involving patient-level imputation will also discussed. All three reference-based imputation approaches will be contrasted with a "dropout=failure" approach in which an extreme "bad" outlier is imputed for all the dropouts followed by quantile regression-based quantile averaging and a nonparametric bootstrap for inference. Two real datasets and simulations will be used to reinforce the key points.

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

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