Handling missing data in a composite outcome with partially observed components: simulation study based on clustered paediatric routine data

Susan Gachau, Edmund Njeru Njagi, Nelson Owuor, Paul Mwaniki, Matteo Quartagno, Rachel Sarguta, Mike English and Philip Ayieko

Journal of Applied Statistics, 2022, vol. 49, issue 9, 2389-2402

Abstract: Composite scores are useful in providing insights and trends about complex and multidimensional quality of care processes. However, missing data in subcomponents may hinder the overall reliability of a composite measure. In this study, strategies for handling missing data in Paediatric Admission Quality of Care (PAQC) score, an ordinal composite outcome, were explored through a simulation study. Specifically, the implications of the conventional method employed in addressing missing PAQC score subcomponents, consisting of scoring missing PAQC score components with a zero, and a multiple imputation (MI)-based strategy, were assessed. The latent normal joint modelling MI approach was used for the latter. Across simulation scenarios, MI of missing PAQC score elements at item level produced minimally biased estimates compared to the conventional method. Moreover, regression coefficients were more prone to bias compared to standards errors. Magnitude of bias was dependent on the proportion of missingness and the missing data generating mechanism. Therefore, incomplete composite outcome subcomponents should be handled carefully to alleviate potential for biased estimates and misleading inferences. Further research on other strategies of imputing at the component and composite outcome level and imputing compatibly with the substantive model in this setting, is needed.

Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:taf:japsta:v:49:y:2022:i:9:p:2389-2402

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DOI: 10.1080/02664763.2021.1895087

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