Calibrating validation samples when accounting for measurement error in intervention studies

Benjamin Ackerman*, Juned Siddique, Elizabeth A. Stuart

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Many lifestyle intervention trials depend on collecting self-reported outcomes, such as dietary intake, to assess the intervention’s effectiveness. Self-reported outcomes are subject to measurement error, which impacts treatment effect estimation. External validation studies measure both self-reported outcomes and accompanying biomarkers, and can be used to account for measurement error. However, in order to account for measurement error using an external validation sample, an assumption must be made that the inferences are transportable from the validation sample to the intervention trial of interest. This assumption does not always hold. In this paper, we propose an approach that adjusts the validation sample to better resemble the trial sample, and we also formally investigate when bias due to poor transportability may arise. Lastly, we examine the performance of the methods using simulation, and illustrate them using PREMIER, a lifestyle intervention trial measuring self-reported sodium intake as an outcome, and OPEN, a validation study measuring both self-reported diet and urinary biomarkers.

Original languageEnglish (US)
JournalStatistical Methods in Medical Research
DOIs
StateAccepted/In press - 2021

Keywords

  • Lifestyle intervention trial
  • measurement error
  • nutrition
  • propensity scores
  • transportability

ASJC Scopus subject areas

  • Epidemiology
  • Statistics and Probability
  • Health Information Management

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