Moskowitz Chaya S, Pepe Margaret S
Department of Epidemiology and Biostatistics, Memorial Sloan-Kettering Cancer Center, 307 E 63rd Street, 3rd Floor, New York, NY 10021, USA.
Biostatistics. 2004 Jan;5(1):113-27. doi: 10.1093/biostatistics/5.1.113.
The positive and negative predictive values are standard ways of quantifying predictive accuracy when both the outcome and the prognostic factor are binary. Methods for comparing the predictive values of two or more binary factors have been discussed previously (Leisenring et al., 2000, Biometrics 56, 345-351). We propose extending the standard definitions of the predictive values to accommodate prognostic factors that are measured on a continuous scale and suggest a corresponding graphical method to summarize predictive accuracy. Drawing on the work of Leisenring et al. we make use of a marginal regression framework and discuss methods for estimating these predictive value functions and their differences within this framework. The methods presented in this paper have the potential to be useful in a number of areas including the design of clinical trials and health policy analysis.
当结果和预后因素均为二元变量时，阳性预测值和阴性预测值是量化预测准确性的标准方法。先前已讨论过比较两个或多个二元因素预测值的方法（Leisenring等人，2000年，《生物统计学》56卷，345 - 351页）。我们建议扩展预测值的标准定义，以适应在连续尺度上测量的预后因素，并提出一种相应的图形方法来总结预测准确性。借鉴Leisenring等人的工作，我们使用边际回归框架，并讨论在该框架内估计这些预测值函数及其差异的方法。本文提出的方法有可能在包括临床试验设计和卫生政策分析在内的多个领域中发挥作用。
