Prentice R L
Environ Health Perspect. 1985 Nov;63:225-34. doi: 10.1289/ehp.8563225.
Relative risk regression methods are described. These methods provide a unified approach to a range of data analysis problems in environmental risk assessment and in the study of disease risk factors more generally. Relative risk regression methods are most readily viewed as an outgrowth of Cox's regression and life model. They can also be viewed as a regression generalization of more classical epidemiologic procedures, such as that due to Mantel and Haenszel. In the context of an epidemiologic cohort study, relative risk regression methods extend conventional survival data methods and binary response (e.g., logistic) regression models by taking explicit account of the time to disease occurrence while allowing arbitrary baseline disease rates, general censorship, and time-varying risk factors. This latter feature is particularly relevant to many environmental risk assessment problems wherein one wishes to relate disease rates at a particular point in time to aspects of a preceding risk factor history. Relative risk regression methods also adapt readily to time-matched case-control studies and to certain less standard designs. The uses of relative risk regression methods are illustrated and the state of development of these procedures is discussed. It is argued that asymptotic partial likelihood estimation techniques are now well developed in the important special case in which the disease rates of interest have interpretations as counting process intensity functions. Estimation of relative risks processes corresponding to disease rates falling outside this class has, however, received limited attention. The general area of relative risk regression model criticism has, as yet, not been thoroughly studied, though a number of statistical groups are studying such features as tests of fit, residuals, diagnostics and graphical procedures. Most such studies have been restricted to exponential form relative risks as have simulation studies of relative risk estimation procedures with moderate numbers of disease events.
本文描述了相对风险回归方法。这些方法为环境风险评估以及更广泛的疾病风险因素研究中的一系列数据分析问题提供了统一的方法。相对风险回归方法最容易被视为考克斯回归和生存模型的衍生。它们也可以被视为更经典的流行病学程序（如曼特尔和海恩泽尔提出的程序）的回归推广。在流行病学队列研究的背景下，相对风险回归方法通过明确考虑疾病发生时间，同时允许任意基线疾病率、一般截尾和随时间变化的风险因素，扩展了传统的生存数据方法和二元响应（如逻辑）回归模型。后一个特征与许多环境风险评估问题特别相关，在这些问题中，人们希望将特定时间点的疾病率与先前风险因素历史的各个方面联系起来。相对风险回归方法也很容易适用于时间匹配的病例对照研究和某些不太标准的设计。文中举例说明了相对风险回归方法的用途，并讨论了这些程序的发展状况。有人认为，在感兴趣的疾病率可解释为计数过程强度函数的重要特殊情况下，渐近部分似然估计技术现已得到充分发展。然而，对应于不属于此类的疾病率的相对风险过程的估计受到的关注有限。相对风险回归模型批评的总体领域尚未得到彻底研究，尽管一些统计小组正在研究诸如拟合优度检验、残差、诊断和图形程序等特征。大多数此类研究仅限于指数形式的相对风险，相对风险估计程序的模拟研究也限于疾病事件数量适中的情况。
