A comparison of multiple imputation methods for handling missing values in longitudinal data in the presence of a time-varying covariate with a non-linear association with time: A simulation study

AP De Silva; M Moreno-Betancur; AM De Livera; KJ Lee; JA Simpson

Journal article

A comparison of multiple imputation methods for handling missing values in longitudinal data in the presence of a time-varying covariate with a non-linear association with time: A simulation study

AP De Silva, M Moreno-Betancur, AM De Livera, KJ Lee, JA Simpson

BMC Medical Research Methodology | BMC | Published : 2017

DOI: 10.1186/s12874-017-0372-y

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Abstract

Background: Missing data is a common problem in epidemiological studies, and is particularly prominent in longitudinal data, which involve multiple waves of data collection. Traditional multiple imputation (MI) methods (fully conditional specification (FCS) and multivariate normal imputation (MVNI)) treat repeated measurements of the same time-dependent variable as just another 'distinct' variable for imputation and therefore do not make the most of the longitudinal structure of the data. Only a few studies have explored extensions to the standard approaches to account for the temporal structure of longitudinal data. One suggestion is the two-fold fully conditional specification (two-fold FC..

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University of Melbourne Researchers

Anurika De Silva Author

Margarita Moreno-Betancur Author

Kate Lee Author

Julie Simpson Author

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Awarded by National Health and Medical Research Council

Funding Acknowledgements

This work was supported by funding from the National Health and Medical Research Council: a Centre of Research Excellence grant, ID 1035261, awarded to the Victorian Centre of Biostatistics (ViCBiostat); and a Senior Research Fellowship ID 1104975 (JAS) and Career Development Fellowship ID 1053609 (KJL). APDS is funded by a Victorian International Research Scholarship and a Melbourne International Fee Remission Scholarship.