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Bayesian sensitivity analysis to unmeasured confounding for misclassified data
AStA Advances in Statistical Analysis ( IF 1.4 ) Pub Date : 2019-09-18 , DOI: 10.1007/s10182-019-00357-1
Qi Zhou , Yoo-Mi Chin , James D. Stamey , Joon Jin Song

Bayesian sensitivity analysis of unmeasured confounding is proposed for observational data with misclassified outcome. The approach simultaneously corrects bias from error in the outcome and examines possible change in the exposure effect estimation assuming the presence of a binary unmeasured confounder. We assess the influence of unmeasured confounding on the exposure effect estimation through two sensitivity parameters that characterize the associations of the unmeasured confounder with the exposure status and with the outcome variable. The proposed approach is illustrated in the study of the effect of female employment status on the likelihood of domestic violence. An extensive simulation study is conducted to confirm the efficacy of the proposed approach. The simulation results indicate accounting for misclassification in outcome and unmeasured confounding significantly reduce the bias in exposure effect estimation and improve the coverage probability of credible intervals.



中文翻译:

针对错误分类数据的不可测混杂的贝叶斯敏感性分析

针对结果分类错误的观测数据,提出了不可测混杂的贝叶斯敏感性分析。该方法同时纠正了结果误差带来的偏差,并假设存在未测量的二进制混杂因素,从而检查了暴露效应估计中可能发生的变化。我们通过两个敏感度参数评估未测混杂因素对暴露效果估计的影响,这两个敏感性参数表征了未测混杂因素与暴露状态和结果变量之间的关联。在研究女性就业状况对家庭暴力可能性的影响中说明了所建议的方法。进行了广泛的模拟研究,以确认所提出方法的有效性。

更新日期:2019-09-18
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