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

2 Scopus citations


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)
Pages (from-to)1235-1248
Number of pages14
JournalStatistical Methods in Medical Research
Issue number5
StatePublished - May 2021


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

ASJC Scopus subject areas

  • Epidemiology
  • Statistics and Probability
  • Health Information Management


Dive into the research topics of 'Calibrating validation samples when accounting for measurement error in intervention studies'. Together they form a unique fingerprint.

Cite this