Abstract:
: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.
journal_name
Am J Epidemioljournal_title
American journal of epidemiologyauthors
Koopman L,van der Heijden GJ,Grobbee DE,Rovers MMdoi
10.1093/aje/kwm341subject
Has Abstractpub_date
2008-03-01 00:00:00pages
540-5issue
5eissn
0002-9262issn
1476-6256pii
kwm341journal_volume
167pub_type
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