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
Life
LS - Life Style
ELSS - Earth, Life and Social Sciences
Healthy for Life
Acoustics and Audiology
Healthy Living
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
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