Variance estimation in complex survey sampling for generalized linear models
Variance estimation in complex survey sampling for generalized linear models
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DOI：
10.1111/j.1467-9876.2007.00601.x
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发表时间：
2008-01-01
影响因子：
1.6
通讯作者：
Gonin, Rene
中科院分区：
文献类型：
--
作者：
Natarajan, Sundar;Lipsitz, Stuart R.;Gonin, Rene
复杂的调查抽样通常用于对有限的大群体中的一小部分进行抽样。一般来说，调查的进行是为了使样本中的每个单位（例如受试者）有不同的被选中进入样本的概率。为了使样本对总体具有普遍性，在分析中必须考虑设计和被选为样本的概率。本文主要研究复杂调查数据的非标准回归模型。在我们基于医疗支出小组调查数据的激励例子中，结果变量是受试者的“ 2002年医疗保健总支出”。以往对医疗费用数据的分析表明，方差近似等于均值的1.5次幂，这是一个非标准方差函数。目前，该模型的回归参数在标准统计软件包中难以估计。我们提出了一种简单的两步方法来获得一致的回归参数和方差估计；所提出的方法可以在任何标准抽样调查包中实现。该方法适用于任何阶段的复杂抽样调查。
Complex survey sampling is often used to sample a fraction of a large finite population. In general, the survey is conducted so that each unit (e.g. subject) in the sample has a different probability of being selected into the sample. For generalizability of the sample to the population, both the design and the probability of being selected into the sample must be incorporated in the analysis. In this paper we focus on non-standard regression models for complex survey data. In our motivating example, which is based on data from the Medical Expenditure Panel Survey, the outcome variable is the subject's 'total health care expenditures in the year 2002'. Previous analyses of medical cost data suggest that the variance is approximately equal to the mean raised to the power of 1.5, which is a non-standard variance function. Currently, the regression parameters for this model cannot be easily estimated in standard statistical software packages. We propose a simple two-step method to obtain consistent regression parameter and variance estimates; the method proposed can be implemented within any standard sample survey package. The approach is applicable to complex sample surveys with any number of stages.