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dc.contributor.authorDe Silva, AP
dc.contributor.authorMoreno-Betancur, M
dc.contributor.authorDe Livera, AM
dc.contributor.authorLee, KJ
dc.contributor.authorSimpson, JA
dc.date.accessioned2020-12-21T01:33:17Z
dc.date.available2020-12-21T01:33:17Z
dc.date.issued2017-07-25
dc.identifierpii: 10.1186/s12874-017-0372-y
dc.identifier.citationDe Silva, A. P., Moreno-Betancur, M., De Livera, A. M., Lee, K. J. & Simpson, J. A. (2017). 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. BMC MEDICAL RESEARCH METHODOLOGY, 17 (1), https://doi.org/10.1186/s12874-017-0372-y.
dc.identifier.issn1471-2288
dc.identifier.urihttp://hdl.handle.net/11343/256559
dc.description.abstractBACKGROUND: 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 FCS) algorithm, which restricts the imputation of a time-dependent variable to time blocks where the imputation model includes measurements taken at the specified and adjacent times. To date, no study has investigated the performance of two-fold FCS and standard MI methods for handling missing data in a time-varying covariate with a non-linear trajectory over time - a commonly encountered scenario in epidemiological studies. METHODS: We simulated 1000 datasets of 5000 individuals based on the Longitudinal Study of Australian Children (LSAC). Three missing data mechanisms: missing completely at random (MCAR), and a weak and a strong missing at random (MAR) scenarios were used to impose missingness on body mass index (BMI) for age z-scores; a continuous time-varying exposure variable with a non-linear trajectory over time. We evaluated the performance of FCS, MVNI, and two-fold FCS for handling up to 50% of missing data when assessing the association between childhood obesity and sleep problems. RESULTS: The standard two-fold FCS produced slightly more biased and less precise estimates than FCS and MVNI. We observed slight improvements in bias and precision when using a time window width of two for the two-fold FCS algorithm compared to the standard width of one. CONCLUSION: We recommend the use of FCS or MVNI in a similar longitudinal setting, and when encountering convergence issues due to a large number of time points or variables with missing values, the two-fold FCS with exploration of a suitable time window.
dc.languageEnglish
dc.publisherBMC
dc.rights.urihttps://creativecommons.org/licenses/by/4.0
dc.titleA 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
dc.typeJournal Article
dc.identifier.doi10.1186/s12874-017-0372-y
melbourne.affiliation.departmentMelbourne School of Population and Global Health
melbourne.affiliation.departmentPaediatrics (RCH)
melbourne.source.titleBMC Medical Research Methodology
melbourne.source.volume17
melbourne.source.issue1
melbourne.identifier.nhmrc1104975
dc.rights.licenseCC BY
melbourne.elementsid1224224
melbourne.contributor.authorMoreno-Betancur, Margarita
melbourne.contributor.authorde Livera, Alysha
melbourne.contributor.authorSimpson, Julie
melbourne.contributor.authorLee, Katherine
melbourne.contributor.authorDe Silva, Anurika
dc.identifier.eissn1471-2288
melbourne.identifier.fundernameidMURDOCH CHILDRENS RESEARCH INSTITUTE, 1035261
melbourne.identifier.fundernameidNHMRC, 1104975
melbourne.accessrightsOpen Access


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