Measurement error correction and sensitivity analysis in longitudinal dietary intervention studies using an external validation study.
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.
['Siddique|Juned|J|0000-0002-1501-4152', 'Daniels|Michael J|MJ|', 'Carroll|Raymond J|RJ|', 'Raghunathan|Trivellore E|TE|', 'Stuart|Elizabeth A|EA|', 'Freedman|Laurence S|LS|']
D015982:Bias D015415:Biomarkers D004032:Diet D004435:Eating D006801:Humans D008137:Longitudinal Studies D015233:Models, Statistical D000076206:Patient Generated Health Data D015203:Reproducibility of Results D012964:Sodium
recovery biomarker Multiple imputation sodium intake 24-hour dietary recall