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 intervention data with auxiliary data from an external biomarker validation study 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 missing data and multiple imputation is used to fill in the missing measurements. Since most 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. We apply our methods to self-reported sodium intake from the PREMIER study, a multi-component lifestyle intervention trial.
Original language | English (US) |
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Pages (from-to) | 927-937 |
Number of pages | 11 |
Journal | Biometrics |
Volume | 75 |
Issue number | 3 |
DOIs | |
State | Published - Sep 1 2019 |
Funding
Siddique and Stuart's work was supported by NIH R01 HL127491. Daniels’ work was partially supported by NIH CA183854. Carroll's research was supported by a grant from the National Cancer Institute (U01-CA057030). Additional supporting information including synthetic versions of the OPEN and PREMIER data sets and R functions to implement the methods used in their analysis in Section 6 may be found online in the Supporting Information section at the end of the article.
Keywords
- 24-hour dietary recall
- Multiple imputation
- recovery biomarker
- sodium intake
ASJC Scopus subject areas
- General Agricultural and Biological Sciences
- Applied Mathematics
- General Biochemistry, Genetics and Molecular Biology
- General Immunology and Microbiology
- Statistics and Probability