Abstract:
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We compare 4 methods of multiple imputation (MI) by fully conditional specification (FCS) to estimate means and correlations of the health aging index (HAI) over time. HAI is a score-variable based on 5 clinical components. We simulate multivariate normal data for each component at 4 time points across 14 years using within and across-time correlation patterns and percent of missingness representing observed Framingham Heart Study data. Our methods of MI are cross-sectional FCS (XFCS, imputation model uses other components at same time), longitudinal FCS (LFCS, uses same component at all times ignoring cross-component correlation), all FCS (AFCS, uses all components at all times) and 2-fold FCS (2fFCS, uses all components at current and adjacent times). We compare results for multiple sample sizes (n=100/1000), number of imputations (m=5/20) and mechanisms of missingness (MCAR/MAR/MNAR). All but XFCS produce unbiased estimates of means and correlations and yield nearly identical results. Increase in precision is small when increasing from 5 to 20 imputations. Ongoing work explores the MI by FCS effect on using linear mixed effects models to estimate the slope of HAI over time.
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