Zheng Yingye, Heagerty Patrick J, Hsu Li, Newcomb Polly A
Biostatistics and Biomathematics Program, Fred Hutchinson Cancer Research Center, Seattle, Washington 98109, USA.
Biometrics. 2010 Dec;66(4):1024-33. doi: 10.1111/j.1541-0420.2010.01393.x.
Combining data collected from different sources can potentially enhance statistical efficiency in estimating effects of environmental or genetic factors or gene-environment interactions. However, combining data across studies becomes complicated when data are collected under different study designs, such as family-based and unrelated individual-based case-control design. In this article, we describe likelihood-based approaches that permit the joint estimation of covariate effects on disease risk under study designs that include cases, relatives of cases, and unrelated individuals. Our methods accommodate familial residual correlation and a variety of ascertainment schemes. Extensive simulation experiments demonstrate that the proposed methods for estimation and inference perform well in realistic settings. Efficiencies of different designs are contrasted in the simulation. We applied the methods to data from the Colorectal Cancer Family Registry.
整合从不同来源收集的数据可能会提高估计环境或遗传因素的影响或基因 - 环境相互作用时的统计效率。然而，当数据是在不同的研究设计下收集时，比如基于家系和基于无关个体的病例对照设计，跨研究整合数据就变得复杂起来。在本文中，我们描述了基于似然性的方法，这些方法允许在包含病例、病例亲属和无关个体的研究设计下联合估计协变量对疾病风险的影响。我们的方法考虑了家族性残余相关性和多种确定方案。广泛的模拟实验表明，所提出的估计和推断方法在实际环境中表现良好。模拟中对比了不同设计的效率。我们将这些方法应用于来自结直肠癌家族登记处的数据。
