Witte J S, Greenland S
Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, OH 44109-1998, USA.
Stat Med. 1996 Jun 15;15(11):1161-70. doi: 10.1002/(SICI)1097-0258(19960615)15:11<1161::AID-SIM221>3.0.CO;2-7.
Hierarchical regression - which attempts to improve standard regression estimates by adding a second-stage 'prior' regression to an ordinary model - provides a practical approach to evaluating multiple exposures. We present here a simulation study of logistic regression in which we compare hierarchical regression fitted by a two-stage procedure to ordinary maximum likelihood. The simulations were based on case-control data on diet and breast cancer, where the hierarchical model uses a second-stage regression to pull conventional dietary-item estimates toward each other when they have similar levels of food constituents. Our results indicate that hierarchical modelling of continuous covariates offers worthwhile improvement over ordinary maximum-likelihood, provided one does not underspecify the second-stage standard deviations.
分层回归——通过在普通模型中添加第二阶段的“先验”回归来尝试改进标准回归估计——为评估多种暴露因素提供了一种实用方法。我们在此展示一项逻辑回归的模拟研究，其中我们将通过两阶段程序拟合的分层回归与普通最大似然法进行比较。这些模拟基于饮食与乳腺癌的病例对照数据，其中分层模型使用第二阶段回归，当常规饮食项目估计具有相似的食物成分水平时，使它们相互靠拢。我们的结果表明，只要不低估第二阶段标准差，对连续协变量进行分层建模比普通最大似然法有显著改进。
