Adjustment for missing data in complex surveys using doubly robust estimation: application to commercial sexual contact among Indian men.
Adjustment for missing data in complex surveys using doubly robust estimation: application to commercial sexual contact among Indian men.
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DOI：
10.1097/ede.0b013e3181f57571
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发表时间：
2010-11
期刊：
影响因子：
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通讯作者：
Murray M
中科院分区：
文献类型：
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作者：
Wirth KE;Tchetgen Tchetgen EJ;Murray M
人口与健康调查计划通过面对面的访谈和复杂的抽样方案，定期收集许多国家具有国家代表性的艾滋病毒相关危险行为信息。如果受访者跳过被认为是社会不受欢迎的行为的问题，这样的采访可能会带来偏见。我们试图实现一个双重稳健的估计器，以纠正在这种情况下的相关缺失数据。我们对2005-2006年印度人口健康调查中自我报告的商业性接触数据应用了3种调整无反应的方法，以估计性活跃男性和女性性工作者之间的性接触流行率。这些方法是逆概率加权回归、结果回归和双重稳健估计-这是最近描述的一种方法，对模型错误指定更稳健。与商业性接触流行率未调整的0.9%(95%可信区间=0.8%-1.0%)相比，使用双稳健估计调整无反应的流行率为1.1%(1.0%-1.2%)。我们发现了类似的估计，通过结果回归和逆概率加权进行了调整。婚姻状况与项目无反应密切相关，纠正无反应导致未婚男性商业性接触的患病率增加近80%(从6.9%增加到12.1%-12.4%)。未能纠正无反应产生了自我报告的商业性接触的偏见。为了便于将这些方法(包括双稳健估计)应用于复杂的调查数据设置，我们提供了分析方差估计以及相应的SAS和MATL AB代码。无论建模假设是否正确，这些方差估计器仍然有效。
The Demographic and Health Survey program routinely collects nationally representative information on HIV-related risk behaviors in many countries, using face-to-face interviews and a complex sampling scheme. If respondents skip questions about behaviors perceived as socially undesirable, such interviews may introduce bias. We sought to implement a doubly robust estimator to correct for dependent missing data in this context. We applied 3 methods of adjustment for nonresponse on self-reported commercial sexual contact data from the 2005–2006 India Demographic Health Survey to estimate the prevalence of sexual contact between sexually active men and female sex workers. These methods were inverse-probability weighted regression, outcome regression, and doubly robust estimation—a recently-described approach that is more robust to model misspecification. Compared with an unadjusted prevalence of 0.9% for commercial sexual contact prevalence (95% confidence interval = 0.8%–1.0%), adjustment for nonresponse using doubly robust estimation yielded a prevalence of 1.1% (1.0%–1.2%). We found similar estimates with adjustment by outcome regression and inverse-probability weighting. Marital status was strongly associated with item nonresponse, and correction for nonresponse led to a nearly 80% increase in the prevalence of commercial sexual contact among unmarried men (from 6.9% to 12.1%–12.4%). Failure to correct for nonresponse produced a bias in self-reported commercial sexual contact. To facilitate the application of these methods (including the doubly robust estimator) to complex survey data settings, we provide analytical variance estimators and the corresponding SAS and MATLAB code. These variance estimators remain valid regardless of whether the modeling assumptions are correct.