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Activity Number: 563 - Multiple Imputation for Measurement Errors and Other Structured Patterns of Missing Data
Type: Topic Contributed
Date/Time: Wednesday, August 2, 2017 : 2:00 PM to 3:50 PM
Sponsor: Survey Research Methods Section
Abstract #322661 View Presentation
Title: Addressing Differential Measurement Error in Self-Reported Dietary Data Using an External Validation Study: Application to a Longitudinal Lifestyle Intervention Trial
Author(s): Juned Siddique* and Raymond Carroll and Michael J Daniels and Trivellore Raghunathan and Elizabeth Stuart and Laurence Freedman
Companies: Northwestern University and Texas A&M University and University of Texas at Austin and University of Michigan School of Public Health and Johns Hopkins University and Gertner Institute for Epidemiology and Health Policy Research
Keywords: multiple imputation ; sensitivity analysis ; Bayesian inference ; missing data ; pattern-mixture model
Abstract:

In lifestyle intervention trials, where the goal is to change a participant's weight or modify their eating behavior, self-reported diet is a longitudinal outcome variable that is subject to measurement error. We propose a statistical framework for correcting for measurement error in longitudinal self-reported dietary data by combining these data with auxiliary data from external biomarker validation studies where both self-reported and recovery biomarkers of dietary intake are available. In this setting, dietary intake measured without error in the intervention trial is treated as missing data and multiple imputation is used to fill-in the missing measurements. Since validation studies are cross-sectional, they do not contain information on whether the nature of the measurement error changes over time or differs between treatment and control groups. We use sensitivity analyses to address the influence of these unverifiable assumptions involving the measurement error process and how they affect inferences regarding the effect of treatment.


Authors who are presenting talks have a * after their name.

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