Multiple Imputation of Missing Composite Outcomes in Longitudinal Data

Aidan G. O’Keeffe, Daniel M. Farewell, Brian D. M. Tom and Vernon T. Farewell
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Aidan G. O’Keeffe: University College London
Daniel M. Farewell: Cardiff University School of Medicine
Brian D. M. Tom: Cambridge Institute of Public Health
Vernon T. Farewell: Cambridge Institute of Public Health

Statistics in Biosciences, 2016, vol. 8, issue 2, No 8, 310-332

Abstract: Abstract In longitudinal randomised trials and observational studies within a medical context, a composite outcome—which is a function of several individual patient-specific outcomes—may be felt to best represent the outcome of interest. As in other contexts, missing data on patient outcome, due to patient drop-out or for other reasons, may pose a problem. Multiple imputation is a widely used method for handling missing data, but its use for composite outcomes has been seldom discussed. Whilst standard multiple imputation methodology can be used directly for the composite outcome, the distribution of a composite outcome may be of a complicated form and perhaps not amenable to statistical modelling. We compare direct multiple imputation of a composite outcome with separate imputation of the components of a composite outcome. We consider two imputation approaches. One approach involves modelling each component of a composite outcome using standard likelihood-based models. The other approach is to use linear increments methods. A linear increments approach can provide an appealing alternative as assumptions concerning both the missingness structure within the data and the imputation models are different from the standard likelihood-based approach. We compare both approaches using simulation studies and data from a randomised trial on early rheumatoid arthritis patients. Results suggest that both approaches are comparable and that for each, separate imputation offers some improvement on the direct imputation of a composite outcome.

Keywords: Composite outcome; Linear increments; Longitudinal data; Missing data; Multiple imputation (search for similar items in EconPapers)
Date: 2016
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DOI: 10.1007/s12561-016-9146-z

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