Mossman D, Berger J O
Division of Forensic Psychiatry, Wright State University School of Medicine, Dayton, Ohio, 45401-0927, USA.
Med Decis Making. 2001 Nov-Dec;21(6):498-507. doi: 10.1177/0272989X0102100608.
Several medical articles discuss methods of constructing confidence intervals for single proportions and the likelihood ratio, but scant attention has been given to the systematic study of intervals for the posterior odds, or the positive predictive value, of a test.
The authors describe 5 methods of constructing confidence intervals for posttest probabilities when estimates of sensitivity, specificity, and the pretest probability of a disorder are derived from empirical data. They then evaluate each method to determine how well the intervals' coverage properties correspond to their nominal value.
When the estimates of pretest probabilities, sensitivity, and specificity are derived from more than 80 subjects and are not close to 0 or 1, all methods generate intervals with appropriate coverage properties. When these conditions are not met, however, the best-performing method is an objective Bayesian approach implemented by a simple simulation using a spreadsheet.
Physicians and investigators can generate accurate confidence intervals for posttest probabilities in small-sample situations using the objective Bayesian approach.
多篇医学文章讨论了构建单比例置信区间和似然比的方法，但对检验的后验概率或阳性预测值区间的系统研究关注甚少。
作者描述了5种在从经验数据得出敏感性、特异性和疾病的验前概率估计值时构建检验后概率置信区间的方法。然后他们评估每种方法，以确定区间的覆盖特性与其标称值的符合程度。
当验前概率、敏感性和特异性的估计值来自80多个受试者且不接近0或1时，所有方法生成的区间都具有适当的覆盖特性。然而，当这些条件不满足时，表现最佳的方法是通过使用电子表格进行简单模拟实现的客观贝叶斯方法。
医生和研究人员可以使用客观贝叶斯方法在小样本情况下生成检验后概率的准确置信区间。
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