Propagation of uncertainty in Bayesian diagnostic test interpretation.
Propagation of uncertainty in Bayesian diagnostic test interpretation.
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DOI：
10.1097/smj.0b013e3182621a2c
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发表时间：
2012-09
影响因子：
1.1
通讯作者：
Bianchi MT
中科院分区：
文献类型：
--
作者：
Srinivasan P;Westover MB;Bianchi MT
诊断测试结果的贝叶斯解释通常涉及测试前概率和测试结果对应的似然比的点估计;然而，在临床情况下，考虑一系列可能的值来表示这些参数估计的不确定性可能更合适。因此，我们试图证明如何在灵敏度，特异性和疾病预测试概率的不确定性，可以容纳在贝叶斯解释的诊断测试。我们调查了三个问题：假设前测概率的点估计，似然比的不确定性如何传播到后测概率范围？检测灵敏度和特异性的不确定性如何影响似然比的不确定性？当不确定性同时存在于预检验概率和似然比中时，不确定性是如何传播的？似然比不确定性的传播取决于预测试概率，并且对于意外的测试结果更加突出。当这些参数接近100%时，灵敏度和特异性的不确定性显著地传播到似然比的计算中;即使是±10%的适度误差也会引起显著的传播。前测概率和似然比中±20%的组合误差显示出适度的后测概率传播，表明临床估计的现实目标范围。结果提供了一个框架，将范围内的不确定性贝叶斯推理。虽然点估计简化了贝叶斯推理的实现，但在这个多步过程中考虑范围时，认识到误差传播的影响是很重要的。
Bayesian interpretation of diagnostic test results usually involves point estimates of the pretest probability and the likelihood ratio corresponding to the test result; however, it may be more appropriate in clinical situations to consider instead a range of possible values to express uncertainty in the estimates of these parameters. We thus sought to demonstrate how uncertainty in sensitivity, specificity, and disease pretest probability can be accommodated in Bayesian interpretation of diagnostic testing. We investigated three questions: How does uncertainty in the likelihood ratio propagate to the posttest probability range, assuming a point estimate of pretest probability? How does uncertainty in the sensitivity and specificity of a test affect uncertainty in the likelihood ratio? How does uncertainty propagate when present in both the pretest probability and the likelihood ratio? Propagation of likelihood ratio uncertainty depends on the pretest probability and is more prominent for unexpected test results. Uncertainty in sensitivity and specificity propagates into the calculation of likelihood ratio prominently as these parameters approach 100%; even modest errors of ±10% caused dramatic propagation. Combining errors of ±20% in the pretest probability and in the likelihood ratio exhibited modest propagation to posttest probability, suggesting a realistic target range for clinical estimations. The results provide a framework for incorporating ranges of uncertainty into Bayesian reasoning. Although point estimates simplify the implementation of Bayesian reasoning, it is important to recognize the implications of error propagation when ranges are considered in this multistep process.