Doubly robust testing and estimation of model-adjusted effect-measure modification with complex survey data.
Doubly robust testing and estimation of model-adjusted effect-measure modification with complex survey data.
复制标题
使用复杂的调查数据对模型调整的效果测量修改进行双稳健测试和估计。
DOI：
10.1002/sim.5532
复制
发表时间：
2013
影响因子：
2
通讯作者：
Andresen,ElenaM
中科院分区：
文献类型：
--
作者：
Zheng,HaoW;Brumback,BabetteA;Lu,Xiaomin;Bouldin,ErinD;Cannell,MichaelB;Andresen,ElenaM
基于模型的标准化能够调整人群平均暴露对结果影响的混杂因素。它需要一个以混杂因素为条件的暴露概率模型（暴露模型）或一个以暴露和混杂因素为条件的结果预期模型（结果模型）。该方法也可应用于估计效应修正因子类别内的平均暴露效应，并测试这些效应是否不同。最近，我们扩展了该方法，用于复杂的调查数据，以估计残疾状况对三个年龄组内医疗保健成本障碍的影响，并测试差异。我们将该方法应用于2007年佛罗里达行为风险因素监测系统调查（BRFSS）的数据。暴露建模和结果建模方法产生了两组截然不同的结果。在本文中，我们开发和应用BRFSS的例子两个双重强大的方法来测试和估计效果修改复杂的调查数据，这些方法只需要这两个模型之一被正确指定。此外，假设至少有一个模型被正确指定，我们可以使用双重稳健方法来开发和应用暴露和结果模型的拟合优度检验。我们比较暴露建模，结果建模，和双重强大的方法方面的模拟研究和BRFSS的例子。版权所有© 2012约翰威利父子有限公司.
Model‐based standardization enables adjustment for confounding of a population‐averaged exposure effect on an outcome. It requires either a model for the probability of the exposure conditional on the confounders (an exposure model) or a model for the expectation of the outcome conditional on the exposure and the confounders (an outcome model). The methodology can also be applied to estimate averaged exposure effects within categories of an effect modifier and to test whether these effects differ or not. Recently, we extended that methodology for use with complex survey data, to estimate the effects of disability status on cost barriers to health care within three age categories and to test for differences. We applied the methodology to data from the 2007 Florida Behavioral Risk Factor Surveillance System Survey (BRFSS). The exposure modeling and outcome modeling approaches yielded two contrasting sets of results. In the present paper, we develop and apply to the BRFSS example two doubly robust approaches to testing and estimating effect modification with complex survey data; these approaches require that only one of these two models be correctly specified. Furthermore, assuming that at least one of the models is correctly specified, we can use the doubly robust approaches to develop and apply goodness‐of‐fit tests for the exposure and outcome models. We compare the exposure modeling, outcome modeling, and doubly robust approaches in terms of a simulation study and the BRFSS example. Copyright © 2012 John Wiley & Sons, Ltd.