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Imputation of systematically missing predictors in an individual participant data meta-analysis: A generalized approach using MICE

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Author: Jolani, S. · Debray, T.P.A. · Koffijberg, H. · Buuren, S. van · Moons, K.G.M.
Type:article
Date:2015
Source:Statistics in Medicine, 11, 34, 1841-1863
Identifier: 524747
doi: doi:10.1002/sim.6451
Keywords: Acoustics and Audiology · IPD meta-analysis · Missing data · Multilevel model · Multiple imputation · Prediction research · Algorithm · Calculation · Controlled study · Covariance · Deep vein thrombosis · Human · Linear system · Mathematical computing · Maximum likelihood method · Multilevel multiple imputation · Multivariate analysis · Multivariate imputation by chained equation · Nonlinear system · Prediction · Prevalence · Probability · Resche Rigon method · Simulation · Statistical distribution · Statistical model · Stratified multiple imputation · Traditional multiple imputation · Healthy for Life · Healthy Living · Life · LS - Life Style · ELSS - Earth, Life and Social Sciences

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.