Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA.
Stat Methods Med Res. 2021 May;30(5):1235-1248. doi: 10.1177/0962280220988574. Epub 2021 Feb 23.
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.
许多生活方式干预试验依赖于收集自我报告的结果，例如饮食摄入，以评估干预的效果。自我报告的结果存在测量误差，这会影响治疗效果的估计。外部验证研究同时测量自我报告的结果和相关的生物标志物，并可用于纠正测量误差。然而，为了使用外部验证样本纠正测量误差，必须假设从验证样本到感兴趣的干预试验的推断是可转移的。但这种假设并不总是成立的。在本文中，我们提出了一种调整验证样本以更好地反映试验样本的方法，并且还正式研究了由于可转移性差可能导致偏差的情况。最后，我们使用模拟检查了这些方法的性能，并使用 PREMIER（一种生活方式干预试验，测量自我报告的钠摄入量作为结果）和 OPEN（一项验证研究，测量自我报告的饮食和尿液生物标志物）进行了说明。
