Huang Hui, Ma Xiaomei, Waagepetersen Rasmus, Holford Theodore R, Wang Rong, Risch Harvey, Mueller Lloyd, Guan Yongtao
Department of Management Science, University of Miami, Coral Gables, FL 33124.
Yale School of Public Health, New Haven, CT 06520.
J Am Stat Assoc. 2014 Jan 1;109(505):11-23. doi: 10.1080/01621459.2013.870904.
We propose a novel two-step procedure to combine epidemiological data obtained from diverse sources with the aim to quantify risk factors affecting the probability that an individual develops certain disease such as cancer. In the first step we derive all possible unbiased estimating functions based on a group of cases and a group of controls each time. In the second step, we combine these estimating functions efficiently in order to make full use of the information contained in data. Our approach is computationally simple and flexible. We illustrate its efficacy through simulation and apply it to investigate pancreatic cancer risks based on data obtained from the Connecticut Tumor Registry, a population-based case-control study, and the Behavioral Risk Factor Surveillance System which is a state-based system of health surveys.
我们提出了一种新颖的两步法，将从不同来源获得的流行病学数据相结合，旨在量化影响个体患某些疾病（如癌症）概率的风险因素。第一步，我们每次根据一组病例和一组对照推导出所有可能的无偏估计函数。第二步，我们有效地合并这些估计函数，以便充分利用数据中包含的信息。我们的方法在计算上简单且灵活。我们通过模拟说明了它的有效性，并将其应用于基于康涅狄格肿瘤登记处、一项基于人群的病例对照研究以及行为风险因素监测系统（一个基于州的健康调查系统）所获得的数据来调查胰腺癌风险。
