Abstract Number:
1876
Submission Type:
Contributed Abstract
Contributed Abstract Type:
Paper
Participants:
Analissa Avila (1), Beth Glenn (2), Roshan Bastani (2), Catherine Crespi (1)
Institutions:
(1) University of California Los Angeles, Department of Biostatistics, N/A, (2) University of California Los Angeles, Department of Health Policy Management, N/A
Co-Author(s):
Beth Glenn
University of California Los Angeles, Department of Health Policy Management
Roshan Bastani
University of California Los Angeles, Department of Health Policy Management
Catherine Crespi
University of California Los Angeles, Department of Biostatistics
First Author:
Analissa Avila
University of California Los Angeles, Department of Biostatistics
Presenting Author:
Abstract Text:
Missing outcome data are common in cluster randomized trials (CRTs) and can occur due to dropout of individuals, termed "sporadically" missing data, or dropout of clusters, termed "systematically" missing data. Multilevel multiple imputation (MI) methods that handle hierarchical data have been developed. However, application of these methods to CRTs is limited. We examined the performance of four multilevel multiple imputation (MI) methods to handle sporadically and systematically missing CRT outcome data via a simulation study. Our findings showed that one multilevel MI method outperformed the others under various scenarios. Using the best performing MI method, we developed methods for conducting sensitivity analysis to test the robustness of inferences under different missing not at random (MNAR) assumptions. The methods allow for different MNAR assumptions for cluster dropout and individual dropout to reflect that they may arise from different missing data mechanisms. Our methods are illustrated using a real data application. The findings lead to recommendations of approaches for handling missingness in cluster randomized trials.
Keywords:
clustered data|missing data|MNAR|multiple imputation|systematically missing|
Sponsors:
Biometrics Section
Tracks:
Missing Data
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