The impact of missing data on analyses of a time-dependent exposure in a longitudinal cohort: a simulation study.
Amalia Karahalios, Laura Baglietto, Katherine J Lee, Dallas R English, John B Carlin, Julie A Simpson
Emerg Themes Epidemiol | Published : 2013
BACKGROUND: Missing data often cause problems in longitudinal cohort studies with repeated follow-up waves. Research in this area has focussed on analyses with missing data in repeated measures of the outcome, from which participants with missing exposure data are typically excluded. We performed a simulation study to compare complete-case analysis with Multiple imputation (MI) for dealing with missing data in an analysis of the association of waist circumference, measured at two waves, and the risk of colorectal cancer (a completely observed outcome). METHODS: We generated 1,000 datasets of 41,476 individuals with values of waist circumference at waves 1 and 2 and times to the events of col..View full abstract