Transportability of Regression Calibration with External Validation Studies for Measurement Error Correction
Zexiang Li, Donna Spiegelman, Molin Wang, Zuoheng Wang, Xin Zhou

TL;DR
This paper investigates when regression calibration using external validation studies provides unbiased estimates of exposure effects in epidemiology, analyzing conditions for transportability and bias reduction.
Contribution
It provides theoretical conditions for the transportability of regression calibration with external validation, including bias assessment under departures from assumptions.
Findings
Regression calibration can be biased if transportability assumptions are violated.
Under most conditions, regression calibration yields lower bias than naive methods.
Simulation and real data confirm theoretical bias reduction benefits.
Abstract
In nutritional and environmental epidemiology, exposures are impractical to measure accurately, while practical measures for these exposures are often subject to substantial measurement error. Regression calibration is among the most used measurement error correction methods with external validation studies. The use of external studies to assess the measurement error process always carries the risk of introducing estimation bias into the main study analysis. Although the transportability of regression calibration is usually assumed for practical epidemiology studies, it has not been well studied. In this work, under the measurement error process with a mixture of Berkson-like and classical-like errors, we investigate conditions under which the effect estimate from regression calibration with an external validation study is unbiased for the association between exposure and health…
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Taxonomy
TopicsHealth, Environment, Cognitive Aging · Occupational and environmental lung diseases · Clinical Laboratory Practices and Quality Control
