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Stat Med. 2016 Nov 10;35(25):4493-4508. doi: 10.1002/sim.7011. Epub 2016 Jun 20.

Statistical issues related to dietary intake as the response variable in intervention trials.

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Department of Medical Statistics, London School of Hygiene and Tropical Medicine, London, U.K..
Department of Statistics, Texas A&M University, 3143 TAMU, College Station, TX, 77843-3143, U.S.A.
School of Mathematical and Physical Sciences, University of Technology Sydney, Broadway, New South Wales, 2007, Australia.
Department of Biostatistical Sciences, Wake Forest School of Medicine, Winston-Salem, NC, U.S.A.
School of Public Health and Health Systems, University of Waterloo, Waterloo, Ontario, Canada.
Information Management Services, Inc., Rockville, MD, U.S.A.
Biostatistics Unit, Gertner Institute for Epidemiology and Health Policy Research, Sheba Medical Center, Ramat Gan, Israel.


The focus of this paper is dietary intervention trials. We explore the statistical issues involved when the response variable, intake of a food or nutrient, is based on self-report data that are subject to inherent measurement error. There has been little work on handling error in this context. A particular feature of self-reported dietary intake data is that the error may be differential by intervention group. Measurement error methods require information on the nature of the errors in the self-report data. We assume that there is a calibration sub-study in which unbiased biomarker data are available. We outline methods for handling measurement error in this setting and use theory and simulations to investigate how self-report and biomarker data may be combined to estimate the intervention effect. Methods are illustrated using data from the Trial of Nonpharmacologic Intervention in the Elderly, in which the intervention was a sodium-lowering diet and the response was sodium intake. Simulations are used to investigate the methods under differential error, differing reliability of self-reports relative to biomarkers and different proportions of individuals in the calibration sub-study. When the reliability of self-report measurements is comparable with that of the biomarker, it is advantageous to use the self-report data in addition to the biomarker to estimate the intervention effect. If, however, the reliability of the self-report data is low compared with that in the biomarker, then, there is little to be gained by using the self-report data. Our findings have important implications for the design of dietary intervention trials. © 2016 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.


biomarker; differential error; intervention trial; measurement error; self-report; sodium intake

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