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American Journal of Epidemiology Advance Access published online on January 9, 2008
American Journal of Epidemiology, doi:10.1093/aje/kwm341
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American Journal of Epidemiology © The Author 2008. Published by the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oxfordjournals.org.

Practice of Epidemiology

Comparison of Methods of Handling Missing Data in Individual Patient Data Meta-analyses: An Empirical Example on Antibiotics in Children with Acute Otitis Media

Laura Koopman1, Geert J. M. G. van der Heijden1, Diederick E. Grobbee1 and Maroeska M. Rovers1,2

1 Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, the Netherlands
2 Department of Otolaryngology, Wilhelmina Children's Hospital, University Medical Center Utrecht, Utrecht, the Netherlands

Correspondence to Laura Koopman, Stratenum room 6.131, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, P.O. Box 85500, 3508 GA Utrecht, the Netherlands (e-mail: l.koopman{at}umcutrecht.nl).

Received for publication March 28, 2007. Accepted for publication October 25, 2007.

What is the influence of various methods of handling missing data (complete case analyses, single imputation within and over trials, and multiple imputations within and over trials) on the subgroup effects of individual patient data meta-analyses? An empirical data set was used to compare these five methods regarding the subgroup results. Logistic regression analyses were used to determine interaction effects (regression coefficients, standard errors, and p values) between subgrouping variables and treatment. Stratified analyses were performed to determine the effects in subgroups (rate ratios, rate differences, and their 95% confidence intervals). Imputation over trials resulted in different regression coefficients and standard errors of the interaction term as compared with imputation within trials and complete case analyses. Significant interaction effects were found for complete case analyses and imputation within trials, whereas imputation over trials often showed no significant interaction effect. Imputation of missing data over trials might lead to bias, because association of covariates might differ across the included studies. Therefore, despite the gain in statistical power, imputation over trials is not recommended. In the authors' empirical example, imputation within trials appears to be the most appropriate approach of handling missing data in individual patient data meta-analyses.

imputation; meta-analysis; missing data; review [publication type]

Abbreviations: AOM, acute otitis media; IPD, individual patient data


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