Print Email Facebook Twitter Imputation of systematically missing predictors in an individual participant data meta-analysis: A generalized approach using MICE Title Imputation of systematically missing predictors in an individual participant data meta-analysis: A generalized approach using MICE Author Jolani, S. Debray, T.P.A. Koffijberg, H. van Buuren, S. Moons, K.G.M. Publication year 2015 Abstract Individual participant data meta-analyses (IPD-MA) are increasingly used for developing and validating multivariable (diagnostic or prognostic) risk prediction models. Unfortunately, some predictors or even outcomes may not have been measured in each study and are thus systematically missing in some individual studies of the IPD-MA. As a consequence, it is no longer possible to evaluate between-study heterogeneity and to estimate study-specific predictor effects, or to include all individual studies, which severely hampers the development and validation of prediction models.Here, we describe a novel approach for imputing systematically missing data and adopt a generalized linear mixed model to allow for between-study heterogeneity. This approach can be viewed as an extension of Resche-Rigon's method (Stat Med 2013), relaxing their assumptions regarding variance components and allowing imputation of linear and nonlinear predictors.We illustrate our approach using a case study with IPD-MA of 13 studies to develop and validate a diagnostic prediction model for the presence of deep venous thrombosis. We compare the results after applying four methods for dealing with systematically missing predictors in one or more individual studies: complete case analysis where studies with systematically missing predictors are removed, traditional multiple imputation ignoring heterogeneity across studies, stratified multiple imputation accounting for heterogeneity in predictor prevalence, and multilevel multiple imputation (MLMI) fully accounting for between-study heterogeneity.We conclude that MLMI may substantially improve the estimation of between-study heterogeneity parameters and allow for imputation of systematically missing predictors in IPD-MA aimed at the development and validation of prediction models. © 2015 John Wiley & Sons, Ltd. Subject LifeLS - Life StyleELSS - Earth, Life and Social SciencesHealthy for LifeAcoustics and AudiologyHealthy LivingIPD meta-analysisMissing dataMultilevel modelMultiple imputationPrediction researchAlgorithmCalculationControlled studyCovarianceDeep vein thrombosisHumanLinear systemMathematical computingMaximum likelihood methodMultilevel multiple imputationMultivariate analysisMultivariate imputation by chained equationNonlinear systemPredictionPrevalenceProbabilityResche Rigon methodSimulationStatistical distributionStatistical modelStratified multiple imputationTraditional multiple imputation To reference this document use: http://resolver.tudelft.nl/uuid:efafc53c-591f-400e-9d1b-ba0dcacb6458 DOI https://doi.org/10.1002/sim.6451 TNO identifier 524747 ISSN 0277-6715 Source Statistics in Medicine, 34 (11), 1841-1863 Document type article Files To receive the publication files, please send an e-mail request to TNO Library.