Department of Radiotherapy, Academic Medical Center (AMC)-University of Amsterdam, Meibergdreef 9, 1105 AZ Amsterdam, The Netherlands.
Department of Epidemiology, University of Maastricht, Peter Debyeplein 1, 6229 HA Maastricht, The Netherlands.
J Clin Epidemiol. 2015 Oct;68(10):1129-37. doi: 10.1016/j.jclinepi.2015.05.017. Epub 2015 May 14.
Any diagnostic test has an indication area of prior probabilities wherein the gain in diagnostic certainty outweighs its loss. Here, we investigate whether indication area and the maximum diagnostic gain are robust measures if we assume test dependence, alternative physician's heuristics, and varying patient's utilities.
Three mathematical functions for the dependence of test sensitivity (Se) and specificity (Sp) on the prior disease probability were studied. In addition, three different decision heuristics for further management were explored for the case that "no test" would be done. Finally, the valuation of test outcomes was varied. A sensitivity analysis was performed to determine the impact of the alternative assumptions on the indication area and maximum diagnostic gain.
By assuming test dependence, the indication area shifts to higher priors and increases the maximum diagnostic gain. Decision strategies assuming a "threshold before treat" can inadvertently widen the indication area and increase the maximum diagnostic gain. Varying patient utilities will usually reduce the net diagnostic gain. A sensitivity analysis revealed the robustness of the model.
The indication area and the maximum diagnostic gain are robust measures of test performance and are easier to interpret than the classical performance measures.
任何诊断测试都有一个先验概率的适应证范围，在此范围内，诊断确定性的增益超过了其损失。在这里，我们研究了如果假设测试依赖性、替代医生的启发式方法和患者效用的变化，适应证范围和最大诊断增益是否是稳健的衡量标准。
研究了三种依赖于测试灵敏度（Se）和特异性（Sp）的先验疾病概率的数学函数。此外，还探索了在不进行“无测试”的情况下，进一步管理的三种不同决策启发式方法。最后，测试结果的估值也有所不同。进行了敏感性分析，以确定替代假设对适应证范围和最大诊断增益的影响。
通过假设测试依赖性，可以将适应证范围转移到更高的先验概率，并增加最大诊断增益。假设“治疗前阈值”的决策策略可能会无意中扩大适应证范围并增加最大诊断增益。患者效用的变化通常会降低净诊断增益。敏感性分析显示了该模型的稳健性。
适应证范围和最大诊断增益是测试性能的稳健衡量标准，比经典性能衡量标准更容易解释。
