Abstract
BackgroundCluster randomized trials (CRTs) randomize participants in groups, rather than as individuals and are key tools used to assess interventions in health research where treatment contamination is likely or if individual randomization is not feasible. Two potential major pitfalls exist regarding CRTs, namely handling missing data and not accounting for clustering in the primary analysis. The aim of this review was to evaluate approaches for handling missing data and statistical analysis with respect to the primary outcome in CRTs.MethodsWe systematically searched for CRTs published between August 2013 and July 2014 using PubMed, Web of Science, and PsycINFO. For each trial, two independent reviewers assessed the extent of the missing data and method(s) used for handling missing data in the primary and sensitivity analyses. We evaluated the primary analysis and determined whether it was at the cluster or individual level.ResultsOf the 86 included CRTs, 80 (93 %) trials reported some missing outcome data. Of those reporting missing data, the median percent of individuals with a missing outcome was 19 % (range 0.5 to 90 %). The most common way to handle missing data in the primary analysis was complete case analysis (44, 55 %), whereas 18 (22 %) used mixed models, six (8 %) used single imputation, four (5 %) used unweighted generalized estimating equations, and two (2 %) used multiple imputation. Fourteen (16 %) trials reported a sensitivity analysis for missing data, but most assumed the same missing data mechanism as in the primary analysis. Overall, 67 (78 %) trials accounted for clustering in the primary analysis.ConclusionsHigh rates of missing outcome data are present in the majority of CRTs, yet handling missing data in practice remains suboptimal. Researchers and applied statisticians should carry out appropriate missing data methods, which are valid under plausible assumptions in order to increase statistical power in trials and reduce the possibility of bias. Sensitivity analysis should be performed, with weakened assumptions regarding the missing data mechanism to explore the robustness of results reported in the primary analysis.Electronic supplementary materialThe online version of this article (doi:10.1186/s13063-016-1201-z) contains supplementary material, which is available to authorized users.
Highlights
Cluster randomized trials (CRTs) randomize participants in groups, rather than as individuals and are key tools used to assess interventions in health research where treatment contamination is likely or if individual randomization is not feasible
Description and handling of missing data We evaluated the degree of missing data and the method(s) for handling missing data in the primary analysis for each trial
If the trial had longitudinal data, we calculated the missing rate at the last time point or time point of the primary analysis if specified. Of those who reported some missing data, we identified the statistical methods used to handle missing data, classified into the following categories: complete case, single imputation, multiple imputation (MI), generalized estimating equations (GEE), mixed model or inverse probability weighting (IPW)
Summary
Cluster randomized trials (CRTs) randomize participants in groups, rather than as individuals and are key tools used to assess interventions in health research where treatment contamination is likely or if individual randomization is not feasible. Two potential major pitfalls exist regarding CRTs, namely handling missing data and not accounting for clustering in the primary analysis. The aim of this review was to evaluate approaches for handling missing data and statistical analysis with respect to the primary outcome in CRTs. In cluster randomized trials (CRTs), groups of participants, rather than individuals, are randomized to intervention arms. Two potential pitfalls with respect to CRTs are handling missing data and not accounting for clustering in the primary analysis. Treatment arm imbalance with respect to missing data is likely to introduce bias when the outcome is related to the reason for patient withdrawal.
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