Am J Epidemiol. 2022 Jun 27;191(7):1290-1299. doi: 10.1093/aje/kwac025.
Data collected from a validation substudy permit calculation of a bias-adjusted estimate of effect that is expected to equal the estimate that would have been observed had the gold standard measurement been available for the entire study population. In this paper, we develop and apply a framework for adaptive validation to determine when sufficient validation data have been collected to yield a bias-adjusted effect estimate with a prespecified level of precision. Prespecified levels of precision are decided a priori by the investigator, based on the precision of the conventional estimate and allowing for wider confidence intervals that would still be substantively meaningful. We further present an applied example of the use of this method to address exposure misclassification in a study of transmasculine/transfeminine youth and self-harm. Our method provides a novel approach to effective and efficient estimation of classification parameters as validation data accrue, with emphasis on the precision of the bias-adjusted estimate. This method can be applied within the context of any parent epidemiologic study design in which validation data will be collected and modified to meet alternative criteria given specific study or validation study objectives.
从验证子研究中收集的数据允许计算出经过偏差调整的效应估计值，该估计值有望与如果整个研究人群都可获得金标准测量值所观察到的估计值相等。在本文中，我们开发并应用了一种自适应验证框架，以确定何时已收集到足够的验证数据，从而可以生成具有预定精度的经过偏差调整的效应估计值。预定的精度水平是由研究人员根据常规估计的精度预先确定的，并允许更宽的置信区间，这些区间仍然具有实质性意义。我们进一步介绍了该方法在跨性别/跨性别青年和自伤研究中解决暴露分类错误的应用实例。我们的方法为随着验证数据的积累，有效地和高效地估计分类参数提供了一种新颖的方法，重点是偏差调整估计的精度。该方法可以应用于任何父母流行病学研究设计的背景下，在该研究设计中，将收集和修改验证数据，以满足特定研究或验证研究目标的替代标准。
