Consider a set of baseline predictors X to predict a binary outcome D and let Y be a novel marker or predictor. This paper is concerned with evaluating the performance of the augmented risk model P(D = 1|Y,X) compared with the baseline model P(D = 1|X). The diagnostic likelihood ratio, DLRX(y), quantifies the change in risk obtained with knowledge of Y = y for a subject with baseline risk factors X. The notion is commonly used in clinical medicine to quantify the increment in risk prediction due to Y. It is contrasted here with the notion of covariate-adjusted effect of Y in the augmented risk model. We also propose methods for making inference about DLRX(y). Case–control study designs are accommodated. The methods provide a mechanism to investigate if the predictive information in Y varies with baseline covariates. In addition, we show that when combined with a baseline risk model and information about the population distribution of Y given X, covariate-specific predictiveness curves can be estimated. These curves are useful to an individual in deciding if ascertainment of Y is likely to be informative or not for him. We illustrate with data from 2 studies: one is a study of the performance of hearing screening tests for infants, and the other concerns the value of serum creatinine in diagnosing renal artery stenosis.
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机译:考虑一组基线预测变量X来预测二元结果D,并使Y为一个新颖的标记或预测变量。本文关注与基准模型P(D = 1 | X)相比,评估增强风险模型P(D = 1 | Y,X)的性能。诊断似然比DLRX(y)对具有基线风险因子X的受试者的Y = y知识所获得的风险变化进行量化。该概念通常在临床医学中用于量化由Y引起的风险预测的增量。在这里与 Y em>的协变量调整效应在增强风险模型中的概念形成对比。我们还提出了推断DLR X em>( y em>)的方法。提供病例对照研究设计。该方法提供了一种机制来研究 Y em>中的预测信息是否随基线协变量而变化。此外,我们表明,与基线风险模型和给定 X em>的 Y em>人口分布信息结合使用时,可以估计出协变量特定的预测曲线。这些曲线对于个人确定 Y em>的确定是否对他有帮助是有用的。我们通过2项研究的数据进行说明:一项是对婴儿听力筛查测试的性能研究,另一项涉及血清肌酐在诊断肾动脉狭窄中的价值。
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