Sato Tosiya, Matsuyama Yutaka
Department of Biostatistics, Kyoto University School of Public Health, Yoshida Konoe-cho, Sakyo-ku, Kyoto 606-8501, Japan.
Epidemiology. 2003 Nov;14(6):680-6. doi: 10.1097/01.EDE.0000081989.82616.7d.
In this article, we show the general relation between standardization methods and marginal structural models. Standardization has been recognized as a method to control confounding and to estimate causal parameters of interest. Because standardization requires stratification by confounders, the sparse-data problem will occur when stratified by many confounders and one then might have an unstable estimator. A new class of causal models called marginal structural models has recently been proposed. In marginal structural models, the parameters are consistently estimated by the inverse-probability-of-treatment weighting method. Marginal structural models give a nonparametric standardization using the total group (exposed and unexposed) as the standard. In epidemiologic analysis, it is also important to know the change in the average risk of the exposed (or the unexposed) subgroup produced by exposure, which corresponds to the exposed (or the unexposed) group as the standard. We propose modifications of the weights in the marginal structural models, which give the nonparametric estimation of standardized parameters. With the proposed weights, we can use the marginal structural models as a useful tool for the nonparametric multivariate standardization.
在本文中，我们展示了标准化方法与边际结构模型之间的一般关系。标准化已被视为一种控制混杂因素并估计感兴趣的因果参数的方法。由于标准化需要按混杂因素进行分层，当按许多混杂因素分层时会出现稀疏数据问题，进而可能导致估计量不稳定。最近提出了一类新的因果模型，称为边际结构模型。在边际结构模型中，参数通过治疗逆概率加权法进行一致估计。边际结构模型使用整个组（暴露组和未暴露组）作为标准进行非参数标准化。在流行病学分析中，了解暴露（或未暴露）亚组的平均风险因暴露而产生的变化也很重要，这对应于以暴露（或未暴露）组作为标准。我们提出了边际结构模型中权重的修改方法，该方法可对标准化参数进行非参数估计。使用所提出的权重，我们可以将边际结构模型用作非参数多变量标准化的有用工具。
