Statistics and Epidemiology Unit, RTI International, Research Triangle Park, North Carolina 27709-2194, USA.
Am J Epidemiol. 2010 Mar 1;171(5):618-23. doi: 10.1093/aje/kwp440. Epub 2010 Feb 4.
There is increasing interest in estimating and drawing inferences about risk or prevalence ratios and differences instead of odds ratios in the regression setting. Recent publications have shown how the GENMOD procedure in SAS (SAS Institute Inc., Cary, North Carolina) can be used to estimate these parameters in non-population-based studies. In this paper, the authors show how model-adjusted risks, risk differences, and risk ratio estimates can be obtained directly from logistic regression models in the complex sample survey setting to yield population-based inferences. Complex sample survey designs typically involve some combination of weighting, stratification, multistage sampling, clustering, and perhaps finite population adjustments. Point estimates of model-adjusted risks, risk differences, and risk ratios are obtained from average marginal predictions in the fitted logistic regression model. The model can contain both continuous and categorical covariates, as well as interaction terms. The authors use the SUDAAN software package (Research Triangle Institute, Research Triangle Park, North Carolina) to obtain point estimates, standard errors (via linearization or a replication method), confidence intervals, and P values for the parameters and contrasts of interest. Data from the 2006 National Health Interview Survey are used to illustrate these concepts.
人们越来越感兴趣的是在回归设置中估计和推断风险或流行率比和差异，而不是odds 比。最近的出版物展示了如何在非基于人群的研究中使用 SAS 中的 GENMOD 过程（SAS Institute Inc.，Cary，北卡罗来纳州）来估计这些参数。在本文中，作者展示了如何直接从复杂抽样调查设置中的逻辑回归模型中获得模型调整后的风险、风险差异和风险比估计值，从而得出基于人群的推论。复杂抽样调查设计通常涉及加权、分层、多阶段抽样、聚类，以及可能的有限人口调整的某种组合。模型调整后的风险、风险差异和风险比的点估计值是从拟合逻辑回归模型中的平均边际预测中获得的。该模型可以包含连续和分类协变量，以及交互项。作者使用 SUDAAN 软件包（Research Triangle Institute，Research Triangle Park，North Carolina）来获得感兴趣的参数和对比的点估计值、标准误差（通过线性化或复制方法）、置信区间和 P 值。使用 2006 年全国健康访谈调查的数据来说明这些概念。
