Multiple imputation methods for bivariate outcomes in cluster randomised trials: Supporting Information

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Diaz-ordaz, K, Kenward, M, Gomes, M and Grieve, R. 2016. Multiple imputation methods for bivariate outcomes in cluster randomised trials: Supporting Information. [Online]. London School of Hygiene & Tropical Medicine, London, United Kingdom. Available from: 10.17037/DATA.99

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Missing observations are common in cluster randomised trials. The problem is exacerbated when modelling bivariate outcomes jointly, as the proportion of complete cases is often considerably smaller than the proportion having either of the outcomes fully observed. Approaches taken to handling such missing data include the following: complete case analysis, single-level multiple imputation that ignores the clustering, multiple imputation with a fixed effect for each cluster and multilevel multiple imputation. We contrasted the alternative approaches to handling missing data in a cost-effectiveness analysis that uses data from a cluster randomised trial to evaluate an exercise intervention for care home residents. We then conducted a simulation study to assess the performance of these approaches on bivariate continuous outcomes, in terms of confidence interval coverage and empirical bias in the estimated treatment effects. Missing-at-random clustered data scenarios were simulated following a full-factorial design.

Published in a 3rd party system Date: 14 March 2016
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Data Creators(s): Diaz-ordaz, K, Kenward, M, Gomes, M and Grieve, R
LSHTM Faculty/Department: Faculty of Epidemiology and Population Health > Dept of Medical Statistics
Faculty of Public Health and Policy > Dept of Health Services Research and Policy
Participating Institutions: London School of Hygiene & Tropical Medicine

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