Prentice R L, Self S G
Fred Hutchinson Cancer Research Center, Seattle, WA 98104.
Stat Med. 1988 Jan-Feb;7(1-2):275-87. doi: 10.1002/sim.4780070127.
Relative risk regression methods provide a unifying and powerful approach to a range of problems in the design and analysis of cohort studies and prevention trials. Standard partial likelihood-based estimation procedures do not, however, encompass several features that are important in such contexts. Specifically, one may wish to relate disease rates marginally to 'recent' risk factor measurements, whereas a partial likelihood approach requires one to condition on an accumulating risk factor history. Secondly, risk factor values may be ascertained with considerable measurement error, thereby requiring specialized procedures to estimate relative risk parameters. Thirdly, analysis of raw materials to obtain desired covariate (risk factor) histories may involve considerable expense if carried out for the entire cohort. Case-control and case-cohort sampling procedures can avoid much of this expense, but once again partial likelihood estimation procedures require generalization. Such generalizations are described herein.
相对风险回归方法为队列研究和预防试验的设计与分析中的一系列问题提供了一种统一且强大的方法。然而，基于标准偏似然的估计程序并未涵盖在此类情况下很重要的几个特征。具体而言，人们可能希望将疾病发生率边际地与“近期”风险因素测量值相关联，而偏似然方法要求人们基于累积的风险因素历史进行条件设定。其次，风险因素值可能存在相当大的测量误差，因此需要专门的程序来估计相对风险参数。第三，如果对整个队列进行分析以获得所需的协变量（风险因素）历史，可能会涉及相当大的费用。病例对照和病例队列抽样程序可以避免大部分此类费用，但偏似然估计程序再次需要进行推广。本文将描述此类推广。
