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Activity Number: 428
Type: Contributed
Date/Time: Tuesday, August 11, 2015 : 2:00 PM to 3:50 PM
Sponsor: Biometrics Section
Abstract #317225 View Presentation
Title: Statistical Analysis and Handling of Missing Data in Cluster Randomized Trials: A Systematic Review
Author(s): Mallorie Fiero* and Shuang Huang and Melanie L. Bell
Companies: The University of Arizona and The University of Arizona and The University of Arizona
Keywords: Cluster randomized trials ; Missing data ; Dropout ; Sensitivity analysis
Abstract:

We evaluated methods used in handling missing data and statistical analysis with respect to the primary outcome in cluster randomized trials (CRTs). We searched for CRTs published between August 2013 and July 2014 using several databases. Of the 86 CRTs included in the review, 80 (93%) trials reported some missing outcome data. Of those reporting missing data, the median percent of missing outcome data was 19% (range 0.5 - 90%). The most common way to handle missing data in the primary analysis was complete case analysis (44, 55%), while 18 (22%) used mixed models, six (8%) used single imputation, four (5%) used un-weighted generalized estimating equations, and two (2%) used multiple imputation. Fourteen (16%) trials reported a sensitivity analysis, most of which did not weaken the missing data assumption from the primary analysis. Overall, 67 (78%) trials accounted for clustering in the primary analysis. High rates of missing outcome data are present in majority of CRTs, yet handling missing data in practice remains suboptimal.


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

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