Journal article
Diagnosing problems with imputation models using the Kolmogorov-Smirnov test: A simulation study
CD Nguyen, JB Carlin, KJ Lee
BMC Medical Research Methodology | Published : 2013
Abstract
Background: Multiple imputation (MI) is becoming increasingly popular as a strategy for handling missing data, but there is a scarcity of tools for checking the adequacy of imputation models. The Kolmogorov-Smirnov (KS) test has been identified as a potential diagnostic method for assessing whether the distribution of imputed data deviates substantially from that of the observed data. The aim of this study was to evaluate the performance of the KS test as an imputation diagnostic. Methods. Using simulation, we examined whether the KS test could reliably identify departures from assumptions made in the imputation model. To do this we examined how the p-values from the KS test behaved when ske..
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Awarded by Murdoch Children's Research Institute
Funding Acknowledgements
This work was supported by funding from the National Health and Medical Research Council: Career Development Fellowship ID 1053609 (KJL), a Centre of Research Excellence grant, ID 1035261, awarded to the Victorian Centre for Biostatistics (ViCBiostat), and Project Grant ID 607400. The authors also acknowledge support provided to the Murdoch Childrens Research Institute through the Victorian Government's Operational Infrastructure Support Program. This paper used unit record data from Growing Up in Australia, the Longitudinal Study of Australian Children. The study is conducted in partnership between the Department of Social Services (DSS), the Australian Institute of Family Studies (AIFS) and the Australian Bureau of Statistics (ABS). The findings and views reported in this paper are those of the author and should not be attributed to DSS, AIFS or the ABS.