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.