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 outcome. We further examine departures from the transportability assumption, under which the regression calibration estimator is itself biased. However, we theoretically prove that, in most cases, it yields lower bias than the naive method. The derived conditions are confirmed through simulation studies and further verified in an example investigating the association between the risk of cardiovascular disease and moderate physical activity in the health professional follow-up study.

翻译：在营养与环境流行病学中，准确测量暴露量往往不切实际，而实际可用的测量方法通常存在显著的测量误差。回归校准是结合外部验证研究最常用的测量误差校正方法之一。使用外部研究评估测量误差过程始终存在将估计偏倚引入主研究分析的风险。尽管在实际流行病学研究中通常假设回归校准具有可移植性，但这一假设尚未得到充分研究。本研究在同时包含类伯克森误差与类经典误差的测量误差过程下，探讨了采用外部验证研究的回归校准所得效应估计量对暴露与健康结局关联保持无偏的条件。我们进一步检验了偏离可移植性假设的情形，此时回归校准估计量本身存在偏倚。然而，我们通过理论证明，在大多数情况下，该方法产生的偏倚低于朴素估计法。通过模拟研究验证了所推导的条件，并在健康专业人员随访研究中通过探究心血管疾病风险与适度体力活动关联的实例进行了进一步验证。