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多阅读者 ROC 方法的效能估计:更新与统一的方法。

Power estimation for multireader ROC methods an updated and unified approach.

机构信息

Iowa City VA Medical Center, IA 52246-2208, USA.

出版信息

Acad Radiol. 2011 Feb;18(2):129-42. doi: 10.1016/j.acra.2010.09.007.

Abstract

RATIONALE AND OBJECTIVES

We describe a step-by-step procedure for estimating power and sample size for planned multireader receiver operating characteristic (ROC) studies that will be analyzed using either the Dorfman-Berbaum-Metz (DBM) or Obuchowski-Rockette (OR) method. This procedure updates previous approaches by incorporating recent methodological developments and unifies the approaches by allowing inputs to be conjectured parameter values or outputs from either a DBM or OR pilot-study analysis.

MATERIALS AND METHODS

Power computations are described in a step-by-step procedure and the theoretical basis for the procedure is described. Updates include using the currently recommended denominator degrees of freedom, accounting for different pilot and planned study normal-to-abnormal case ratios, and a new method for computing the OR test-by-reader variance component.

RESULTS

Using a real dataset we illustrate how to compute the power for two planned studies, one having the same normal-to-abnormal case ratio as the pilot study and the other having a different ratio. In a simulation study, we show that the proposed procedure gives mean power estimates close to the true power.

CONCLUSIONS

Application of the updated procedure is straightforward. It is important that pilot data be comparable to the planned study with respect to the modalities, reader expertise, and case selection. Variability of the power estimates warrants further investigation.

摘要

背景与目的

我们描述了一个逐步的程序,用于估计将使用 Dorfman-Berbaum-Metz(DBM)或 Obuchowski-Rockette(OR)方法进行分析的多阅读者接收器操作特征(ROC)研究的功效和样本量。该程序通过纳入最近的方法学发展和允许输入是 DBM 或 OR 初步研究分析的猜测参数值或输出,更新了以前的方法。

材料与方法

逐步描述了功效计算,并描述了该程序的理论基础。更新包括使用当前推荐的分母自由度,考虑到不同的初步研究和计划研究正常到异常病例的比例,以及用于计算 OR 测试到阅读者方差分量的新方法。

结果

使用真实数据集,我们说明了如何计算两个计划研究的功效,一个研究的正常到异常病例比例与初步研究相同,另一个研究的比例不同。在一项模拟研究中,我们表明,所提出的程序给出的平均功效估计值接近真实功效。

结论

更新后的程序的应用非常简单。重要的是,初步数据在模态、阅读者专业知识和病例选择方面应与计划研究具有可比性。功效估计的可变性需要进一步调查。

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本文引用的文献

1
Prediction accuracy of a sample-size estimation method for ROC studies.
Acad Radiol. 2010 May;17(5):628-38. doi: 10.1016/j.acra.2010.01.007.
2
Recent developments in the Dorfman-Berbaum-Metz procedure for multireader ROC study analysis.
Acad Radiol. 2008 May;15(5):647-61. doi: 10.1016/j.acra.2007.12.015.
3
5
Power estimation for the Dorfman-Berbaum-Metz method.
Acad Radiol. 2004 Nov;11(11):1260-73. doi: 10.1016/j.acra.2004.08.009.
6
"Proper" Binormal ROC Curves: Theory and Maximum-Likelihood Estimation.
J Math Psychol. 1999 Mar;43(1):1-33. doi: 10.1006/jmps.1998.1218.
9
Sample size determination for diagnostic accuracy studies involving binormal ROC curve indices.
Stat Med. 1997 Jul 15;16(13):1529-42. doi: 10.1002/(sici)1097-0258(19970715)16:13<1529::aid-sim565>3.0.co;2-h.

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