Am J Epidemiol. 2013 Dec 1;178(11):1673-9. doi: 10.1093/aje/kwt181. Epub 2013 Sep 12.
Odds ratios are frequently used for estimating the effect of an exposure on the probability of disease in case-control studies. In planning such studies, methods for sample size determination are required to ensure sufficient accuracy in estimating odds ratios once the data are collected. Often, the exposure used in epidemiologic studies is not perfectly ascertained. This can arise from recall bias, the use of a proxy exposure measurement, uncertain work exposure history, and laboratory or other errors. The resulting misclassification can have large impacts on the accuracy and precision of estimators, and specialized estimation techniques have been developed to adjust for these biases. However, much less work has been done to account for the anticipated decrease in the precision of estimators at the design stage. Here, we develop methods for sample size determination for odds ratios in the presence of exposure misclassification by using several interval-based Bayesian criteria. By using a series of prototypical examples, we compare sample size requirements after adjustment for misclassification with those required when this problem is ignored. We illustrate the methods by planning a case-control study of the effect of late introduction of peanut to the diet of children to the subsequent development of peanut allergy.
比值比常用于病例对照研究中估计暴露对疾病概率的影响。在规划此类研究时，需要确定样本量的方法，以确保在收集数据后能够足够准确地估计比值比。在流行病学研究中，暴露通常不能被完美确定。这可能是由于回忆偏倚、使用代理暴露测量、不确定的工作暴露史以及实验室或其他错误。由此产生的错误分类会对估计值的准确性和精度产生重大影响，因此已经开发了专门的估计技术来调整这些偏差。然而，在设计阶段，为了考虑到估计值精度预期下降的问题，所做的工作要少得多。在这里，我们通过使用几种基于区间的贝叶斯标准，开发了在存在暴露错误分类情况下确定比值比样本量的方法。通过使用一系列原型示例，我们比较了在调整错误分类后的样本量要求与忽略该问题时所需的样本量要求。我们通过计划一项病例对照研究来阐明这些方法，该研究旨在研究儿童饮食中晚期引入花生对随后发生花生过敏的影响。
