PURPOSE: 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. METHODS: This method uses local linear interpolation, with multiply 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. RESULTS: Coefficients of random intercept models used to assess the association between annual changes in body mass index and dense breast area estimated with this approach (β = -0.17, p = .46) differed from those obtained when each mammogram was matched to the nearest study visit (β = -0.30, p = .04). The proposed estimation approach had a small average prediction error (0.11 cm2). CONCLUSIONS: Because matching does not incorporate breast density changes over time, our local linear interpolation with multiple imputation approach may provide more accurate results. The proposed approach is applicable to other epidemiologic studies with off-schedule data in which the missing variable changes linearly over relatively short periods of time.
PURPOSE: 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. METHODS: This method uses local linear interpolation, with multiply 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. RESULTS: Coefficients of random intercept models used to assess the association between annual changes in body mass index and dense breast area estimated with this approach (β = -0.17, p = .46) differed from those obtained when each mammogram was matched to the nearest study visit (β = -0.30, p = .04). The proposed estimation approach had a small average prediction error (0.11 cm2). CONCLUSIONS: Because matching does not incorporate breast density changes over time, our local linear interpolation with multiple imputation approach may provide more accurate results. The proposed approach is applicable to other epidemiologic studies with off-schedule data in which the missing variable changes linearly over relatively short periods of time.
Authors: A Rogier T Donders; Geert J M G van der Heijden; Theo Stijnen; Karel G M Moons Journal: J Clin Epidemiol Date: 2006-07-11 Impact factor: 6.437
Authors: Geert J M G van der Heijden; A Rogier T Donders; Theo Stijnen; Karel G M Moons Journal: J Clin Epidemiol Date: 2006-07-11 Impact factor: 6.437
Authors: Carolyn J Crandall; Yan Zheng; Arun Karlamangla; Barbara Sternfeld; Laurel A Habel; Nina Oestreicher; Janet Johnston; Jane A Cauley; Gail A Greendale Journal: Ann Epidemiol Date: 2007-05-29 Impact factor: 3.797
Authors: Katherine W Reeves; Roslyn A Stone; Francesmary Modugno; Roberta B Ness; Victor G Vogel; Joel L Weissfeld; Laurel A Habel; Barbara Sternfeld; Jane A Cauley Journal: Int J Cancer Date: 2009-03-01 Impact factor: 7.396
Authors: Laurel A Habel; Angela M Capra; Nina Oestreicher; Gail A Greendale; Jane A Cauley; Joyce Bromberger; Carolyn J Crandall; Ellen B Gold; Francesmary Modugno; Martine Salane; Charles Quesenberry; Barbara Sternfeld Journal: Menopause Date: 2007 Sep-Oct Impact factor: 2.953