Leisenring W, Pepe M S
Program in Clinical Statistics, Fred Hutchinson Cancer Research Center, Seattle, Washington 98104, USA.
Biometrics. 1998 Jun;54(2):444-52.
The use of diagnostic likelihood ratios has been advocated in the epidemiologic literature for the past decade. Diagnostic likelihood ratios provide valuable information about the predictive properties of a diagnostic test while having the attractive feature of being independent of the prevalence of disease in the study population. We propose a new regression method that allows for direct assessment of covariate effects on likelihood ratios for binary diagnostic tests. This may be particularly useful in assessing how factors that are under the control of the clinician can be altered to maximize the predictive ability of the test. Similarly, patient characteristics that influence the ability of the test to discriminate between diseased and nondiseased subjects may be identified using the regression model. The regression method is flexible in that it can accommodate clustered data arising from a variety of study designs. We illustrate the method with data from an audiology study.
在过去十年中，流行病学文献一直提倡使用诊断似然比。诊断似然比提供了有关诊断测试预测特性的有价值信息，同时具有独立于研究人群中疾病患病率的吸引人的特点。我们提出了一种新的回归方法，该方法允许直接评估协变量对二元诊断测试似然比的影响。这在评估如何改变临床医生可控制的因素以最大化测试的预测能力方面可能特别有用。同样，可以使用回归模型识别影响测试区分患病和未患病受试者能力的患者特征。该回归方法具有灵活性，因为它可以适应来自各种研究设计的聚类数据。我们用一项听力学研究的数据说明了该方法。
