Data in epidemiological studies sometimes are collected off-schedule from planned study visits. In an ancillary study to the Study of Women's Health Across the Nation (SWAN), longitudinal breast density data were collected retrospectively from mammograms that were not acquired at the study visits. We propose a method to estimate the off-schedule breast density measurements at the time of study visits.
This method uses local linear interpolation, withmultiply imputed error terms drawn from assumed subject-specific normal distributions based on the within-subject standard deviations of mammographic density measurements. We evaluate the validity and implications of this approach.
Coefficients of random intercept models assessing the association between annual changes in body mass index and dense breast area estimated with this approach (β=-0.17, P=0.46) differed from those obtained when each mammogram was matched to the nearest study visit (β=-0.30, P=0.04). The proposed estimation approach had a small average prediction error (0.11 cm2).
Because matching does not incorporatebreast density changes over time, ourlocal linear interpolation with multiple imputation approach may provide more accurate results. The proposed approach is applicable to other epidemiologic studies with off-schedule data where the missing variable changes linearly over relatively short periods of time.