Macaskill Petra
Screening and Test Evaluation Program, School of Public Health, University of Sydney, NSW 2006, Australia.
J Clin Epidemiol. 2004 Sep;57(9):925-32. doi: 10.1016/j.jclinepi.2003.12.019.
A range of fixed-effect and random-effects meta-analytic methods are available to obtain summary estimates of measures of diagnostic test accuracy. The hierarchical summary receiver operating characteristic (HSROC) model proposed by Rutter and Gatsonis in 2001 represents a general framework for the meta-analysis of diagnostic test studies that allows different parameters to be defined as a fixed effect or random effects within the same model. The Bayesian method used for fitting the model is complex, however, and the model is not widely used. The objective of this report is to show how the model may be fitted using the SAS procedure NLMIXED and to compare the results to the fully Bayesian analysis using an example.
The HSROC model, its assumptions, and its interpretation are described. The advantages of this model over the usual summary ROC (SROC) regression model are outlined. A complex example is used to compare the estimated SROC curves, expected operating points, and confidence intervals using the alternative approaches to fitting the model.
The empirical Bayes estimates obtained using NLMIXED agree closely with those obtained using the fully Bayesian analysis.
This alternative and more straightforward method for fitting the HSROC model makes the model more accessible to meta-analysts.
有一系列固定效应和随机效应的荟萃分析方法可用于获得诊断试验准确性指标的汇总估计值。2001年Rutter和Gatsonis提出的分层汇总接受者操作特征（HSROC）模型代表了诊断试验研究荟萃分析的一般框架，该框架允许在同一模型中将不同参数定义为固定效应或随机效应。然而，用于拟合该模型的贝叶斯方法很复杂，且该模型未得到广泛应用。本报告的目的是展示如何使用SAS过程NLMIXED拟合该模型，并通过一个例子将结果与完全贝叶斯分析进行比较。
描述了HSROC模型、其假设及其解释。概述了该模型相对于常用汇总ROC（SROC）回归模型的优势。使用一个复杂的例子来比较使用不同方法拟合模型时估计的SROC曲线、预期操作点和置信区间。
使用NLMIXED获得的经验贝叶斯估计值与使用完全贝叶斯分析获得的估计值非常接近。
这种拟合HSROC模型的替代且更直接的方法使荟萃分析人员更容易使用该模型。
