错误分类的敏感性分析：一种图形化方法和一种贝叶斯方法。
Sensitivity analysis of misclassification: a graphical and a Bayesian approach.
作者信息
Chu Haitao, Wang Zhaojie, Cole Stephen R, Greenland Sander
机构信息
Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA.
出版信息
Ann Epidemiol. 2006 Nov;16(11):834-41. doi: 10.1016/j.annepidem.2006.04.001. Epub 2006 Jul 13.
PURPOSE
Misclassification can produce bias in measures of association. Sensitivity analyses have been suggested to explore the impact of such bias, but do not supply formally justified interval estimates.
METHODS
To account for exposure misclassification, recently developed Bayesian approaches were extended to incorporate prior uncertainty and correlation of sensitivity and specificity. Under nondifferential misclassification, a contour plot is used to depict relations among the corrected odds ratio, sensitivity, and specificity.
RESULTS
Methods are illustrated by application to a case-control study of cigarette smoking and invasive pneumococcal disease while varying the distributional assumptions about sensitivity and specificity. Results are compared with those of conventional methods, which do not account for misclassification, and a sensitivity analysis, which assumes fixed sensitivity and specificity.
CONCLUSION
By using Bayesian methods, investigators can incorporate uncertainty about misclassification into probabilistic inferences.
目的
错误分类可在关联度量中产生偏差。有人建议进行敏感性分析以探究此类偏差的影响，但并未提供形式上合理的区间估计。
方法
为了考虑暴露错误分类，将最近开发的贝叶斯方法进行扩展，以纳入先验不确定性以及敏感性和特异性的相关性。在非差异性错误分类情况下，使用等高线图来描绘校正后的比值比、敏感性和特异性之间的关系。
结果
通过将方法应用于一项关于吸烟与侵袭性肺炎球菌疾病的病例对照研究来说明，同时改变关于敏感性和特异性的分布假设。将结果与未考虑错误分类的传统方法以及假设敏感性和特异性固定的敏感性分析结果进行比较。
结论
通过使用贝叶斯方法，研究人员可以将关于错误分类的不确定性纳入概率推断中。
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