Longitudinal associations between different dementia diagnoses and medication use jointly accounting for dropout.

Abstract

Using the National Alzheimer's Coordinating Center data with three years of follow-up, a set of covariate-adjusted models that ignore dropout was fit to complete-case data, and to the whole-cohort data. Additionally, covariate-adjusted joint models with shared random effects accounting for dropout were fit to the whole-cohort data. Multiple medication use was defined as polypharmacy (⩾ five medications), hyperpolypharmacy (⩾ ten medications), and total number of medications. Incident diagnoses were 2,032 for AD, 135 for VD, and 139 for LBD. Percentages of dropout at the end of follow-up were as follows: 71.8% for AD, 81.5% for VD, and 77.7% for LBD. The odds ratio (OR) estimate for hyperpolypharmacy among those with LBD versus AD was 2.19 (0.78, 6.15) when estimated using complete-case data and 3.00 (1.66, 5.40) using whole-cohort data. The OR reduced to 1.41 (0.76, 2.64) when estimated from the joint model accounting for dropout. The OR for polypharmacy using complete-case data differed from the estimates using whole-cohort data. The OR for dementia diagnoses on total number of medications was similar, but non-significant when estimated using complete-case data. Reasons for dropout should be investigated and appropriate statistical methods should be applied to reduce bias in longitudinal studies among high-risk dementia cohorts.