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Many lifestyle intervention trials depend on collecting self-reportedoutcomes, like dietary intake, to assess the intervention's effectiveness.Self-reported outcome measures are subject to measurement error, which couldimpact treatment effect estimation. External validation studies measure bothself-reported outcomes and an accompanying biomarker, and can therefore be usedfor measurement error correction. Most validation data, though, are onlyrelevant for outcomes under control conditions. Statistical methods have beendeveloped to use external validation data to correct for outcome measurementerror under control, and then conduct sensitivity analyses around the errorunder treatment to obtain estimates of the corrected average treatment effect.However, an assumption underlying this approach is that the measurement errorstructure of the outcome is the same in both the validation sample and theintervention trial, so that the error correction is transportable to the trial.This may not always be a valid assumption to make. In this paper, we propose anapproach that adjusts the validation sample to better resemble the trial sampleand thus leads to more transportable measurement error corrections. We alsoformally investigate when bias due to poor transportability may arise. Lastly,we examine the method performance using simulation, and illustrate them usingPREMIER, a multi-arm lifestyle intervention trial measuring self-reportedsodium intake as an outcome, and OPEN, a validation study that measures bothself-reported diet and urinary biomarkers.

Transportability of Outcome Measurement Error Correction: from Validation Studies to Intervention Trials