From the Department of Surgery and Perioperative Care, Dell Medical School at the University of Texas at Austin, Austin, Texas.
Department of Anesthesiology, VU University Medical Center, Amsterdam, the Netherlands.
Anesth Analg. 2018 Oct;127(4):1085-1091. doi: 10.1213/ANE.0000000000003698.
To use a diagnostic test effectively and consistently in their practice, clinicians need to know how well the test distinguishes between those patients who have the suspected acute or chronic disease and those patients who do not. Clinicians are equally interested and usually more concerned whether, based on the results of a screening test, a given patient actually: (1) does or does not have the suspected disease; or (2) will or will not subsequently experience the adverse event or outcome. Medical tests that are performed to screen for a risk factor, diagnose a disease, or to estimate a patient's prognosis are frequently a key component of a clinical research study. Like therapeutic interventions, medical tests require proper analysis and demonstrated efficacy before being incorporated into routine clinical practice. This basic statistical tutorial, thus, discusses the fundamental concepts and techniques related to diagnostic testing and medical decision-making, including sensitivity and specificity, positive predictive value and negative predictive value, positive and negative likelihood ratio, receiver operating characteristic curve, diagnostic accuracy, choosing a best cut-point for a continuous variable biomarker, comparing methods on diagnostic accuracy, and design of a diagnostic accuracy study.
为了在实践中有效地、一致地使用诊断测试，临床医生需要了解该测试在区分疑似急性或慢性疾病患者与无该疾病患者方面的性能如何。临床医生同样感兴趣，并且通常更关心的是，基于筛选测试的结果，给定患者实际上是否：(1)患有或未患有疑似疾病；或(2)随后是否会经历不良事件或结果。用于筛查危险因素、诊断疾病或评估患者预后的医学测试通常是临床研究的一个关键组成部分。与治疗干预一样，在将医学测试纳入常规临床实践之前，需要对其进行适当的分析并证明其疗效。因此，本基本统计教程讨论了与诊断测试和医学决策相关的基本概念和技术，包括灵敏度和特异性、阳性预测值和阴性预测值、阳性和阴性似然比、受试者工作特征曲线、诊断准确性、为连续变量生物标志物选择最佳截断值、比较诊断准确性方法以及诊断准确性研究的设计。
