Coughlin S S, Nass C C, Pickle L W, Trock B, Bunin G
Department of Community and Family Medicine, Georgetown University School of Medicine, Washington, DC 20007.
Am J Epidemiol. 1991 Feb 1;133(3):305-13. doi: 10.1093/oxfordjournals.aje.a115875.
A regression method that utilizes an additive model is proposed for the estimation of attributable risk in case-control studies carried out in defined populations. In contrast to previous multivariate procedures for the estimation of attributable risk, which have utilized logistic regression techniques to adjust for confounding factors, the model assumes an additive relation between the covariates included in the regression equation. As an empirical example, additive and logistic models were fitted to matched case-control data from a population-based study of childhood astrocytoma brain tumors. Although both models fitted the data well, the additive model provided a more satisfactory estimate of the risk attributable to multiple exposures, in the absence of significant additive interaction. In contrast to the results from the logistic model, the adjusted estimates of the risk attributable to each factor included in the additive model summed to the overall estimate for all of the factors considered jointly. Thus, the additive approach provides a useful alternative to existing procedures for the multivariate estimation of attributable risk when the additive model is determined to be appropriate on the basis of goodness-of-fit.
本文提出了一种利用加法模型的回归方法，用于在特定人群中开展的病例对照研究中归因风险的估计。与以往利用逻辑回归技术调整混杂因素来估计归因风险的多变量程序不同，该模型假定回归方程中纳入的协变量之间存在加法关系。作为一个实证例子，将加法模型和逻辑模型应用于一项基于人群的儿童星形细胞瘤脑肿瘤研究的匹配病例对照数据。尽管两个模型对数据的拟合都很好，但在不存在显著加法交互作用的情况下，加法模型对多重暴露所致风险提供了更令人满意的估计。与逻辑模型的结果不同，加法模型中纳入的每个因素的归因风险调整估计值之和等于所有因素联合考虑时的总体估计值。因此，当根据拟合优度确定加法模型合适时，加法方法为现有归因风险多变量估计程序提供了一种有用的替代方法。
