Lyles Robert H, Zhang Fan, Drews-Botsch Carolyn
Department of Biostatistics, The Rollins School of Public Health of Emory University, Atlanta, Georgia 30322, USA.
Epidemiology. 2007 May;18(3):321-8. doi: 10.1097/01.ede.0000260004.49431.70.
Internal validation data offer a well-recognized means to help correct for exposure misclassification or measurement error. When available, external validation data offer the advantage of cost-effectiveness. However, external data are a generally inefficient source of information about misclassification parameters. Furthermore, external data are not necessarily "transportable", for example, if there are differences in the design or target populations of the main and validation studies. Recent work has suggested weighted estimators to simultaneously take advantage of internal and external validation data. We explore efficiency and transportability in the fundamental case of estimating the odds ratio for binary exposure in a case-control setting. Our results support the use of closed-form weighted log odds ratio estimators in place of computationally demanding maximum likelihood estimators under both types of validation study designs (using internal data only, and combining internal and external data). We also provide and assess a formal test of the transportability assumption, and introduce a new log odds ratio estimator that is inherently robust to violation of that assumption. A case-control study of the association between maternal antibiotic use and sudden infant death syndrome provides a real-data example.
内部验证数据提供了一种公认的方法，有助于校正暴露误分类或测量误差。如果有外部验证数据，其具有成本效益的优势。然而，外部数据通常是关于误分类参数的低效信息来源。此外，外部数据不一定“可移植”，例如，如果主要研究和验证研究在设计或目标人群上存在差异。最近的研究提出了加权估计量，以同时利用内部和外部验证数据。我们在病例对照研究中估计二元暴露比值比的基本情况下探讨效率和可移植性。我们的结果支持在两种验证研究设计（仅使用内部数据以及结合内部和外部数据）下，使用封闭形式的加权对数比值比估计量来替代计算要求较高的最大似然估计量。我们还提供并评估了对可移植性假设的正式检验，并引入了一种新的对数比值比估计量，该估计量对违反该假设具有内在的稳健性。一项关于母亲使用抗生素与婴儿猝死综合征之间关联的病例对照研究提供了一个实际数据示例。
