Incorrectly handling missing data can lead to imprecise and biased estimates. We describe the effect of applying different approaches to handling missing data in an analysis of the association between body mass index and all-cause mortality in people with type 2 diabetes. Data from the Scottish diabetes register linked to hospital admissions data and death registrations were used. The analysis was based on people diagnosed with type 2 diabetes between 2004 and 2011 with follow-up until 2014. The association between body mass index and mortality was investigated using Cox proportional hazard models with comparison of findings using four different missing data methods; complete case analysis, two multiple imputation models and nearest neighbour imputation. There were 124,451 cases of type 2 diabetes, among which there were 17,085 deaths during 787,275 person-years of follow-up. Patients with missing data (24.8%) had higher mortality than those without (Adjusted hazard ratio: 1.36 [95% confidence interval: 1.31-1.41]). A U-shaped relationship between body mass index and mortality was observed, with the lowest hazard ratios occurring amongst moderately obese people, regardless of the chosen approach for handling missing data. Missing data may affect absolute and relative risk estimates differently and should be considered in analyses of routine data.
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- Deanery of Molecular, Genetic and Population Health Sciences - Research Fellow
- Usher Institute
- Centre for Population Health Sciences
Person: Academic: Research Active