不同方差比双正态ROC参数估计样本量确定方法准确性探讨

目的 探讨不同方差比双正态参数估计时样本量确定方法的准确性,对最常用样本量估计方法--双正态法所估计样本量的准确性进行评价与修正.方法 采用Monte Carlo模拟试验,分别利用参数法和非参数法计算获得曲线下面积的参数估计值,获得实际所需样本量,对Obuchowski和Mcclish给出的不同方差比双正态ROC参数估计所需样本量的准确性进行评价,依据试验数据采用曲线拟合方法给出修正公式.结果 Obuchowski和Mcclish给出的方法是假定患病组诊断变量XA和非患病组诊断变量XN服从正态分布,样本量计算公式是以ROC曲线下面积估计值服从正态分布为前提导出的,但事实上随ROC曲线实际面积θ逐渐增大,样本估计量偏离正态,导致样本量估计结果不够准确,与实际样本需要量有一定差距.在其他条件相同的情况下,患病组与非患病组诊断变量方差比越大实际所需样本量越多,在患病组与非患病组诊断变量方差比分别为2∶1及3∶1的情况下,用Obuchowski和Mcclish方法计算出的样本量与实际所需样本量相差不是很大.根据Monte Carlo模拟试验的结果,给出了Obuchowski和Mcclish方法计算样本量的修正公式,该修正公式可有效地应用于实际.结论 Obuchowski和Mcclish方法计算的样本量进行ROC参数估计时需要调整,采用Monte Carlo方法估计的样本量,可以有效地进行双正态ROC参数估计,达到诊断试验评价要求.     

非参数法估计ROC曲线下面积

ROC是受试者工作特征（ReceiverOperatingCharacteristic）的缩写。ROC曲线及ROC曲线下面积可作为某一诊断方法准确性评价的指标;通过对同一疾病的多种诊断试验分析比较,这些指标可帮助临床医生筛选出最佳诊断方案[1]。对于ROC曲线的构建及其实际临床意义,文献[2]到已作了详细介绍,本文将采用实例数据具体介绍如何利用简单、实用的非参数法估计与比较ROC曲线下面积。一、ROC曲线下面积的计算本文所指的“诊断”是泛指某对象（如人、仪器、设备、试剂、方法等）对某确定事件作出是正常还是异常（无病还是有病、噪音还是信号等）…     

连续变量诊断试验数据的ROC分析

目的 介绍一种连续诊断变量的ROC回归模型，以及在独立和相关结构下的参数估计方法，给出参数误差估计的Bootstrap方法。方法 应用SAS软件中的GENMOD过程和SAS语言编写的程序实现上述过程，并通过实例说明其应用效果。结果 利用ROC曲线方程，可以扣除协变量对诊断试验结果评价的影响，并能够计算出在不同协变量取值下的ROC曲线下面积，提供更为丰富和可靠的信息。结论 文中给出的ROC回归模型可以有效地用于连续变量诊断试验数据的ROC分析。     

基于贝叶斯估计的诊断试验ROC曲线回归模型

目的 阐明基于贝叶斯估计的ROC曲线回归模型.方法 通过实例对比分析,介绍WinBUGS软件ROC曲线回归模型参数估计与应用.结果 基于贝叶斯估计的ROC曲线回归模型不仅可考虑(平衡)协变量对诊断试验结果准确性评价的影响,而且可计算不同协变量取值条件下的ROC曲线下面积;不同先验分布的选取在一定范围内模型参数估计结果较稳定,可作为临床诊断试验结果分析的依据.结论 基于贝叶斯估计的ROC曲线回归模型,可有效地解决受协变量影响的临床诊断试验准确度评价问题.     

无金标准条件下含协变量的ROC曲线估计方法

目的阐明无金标准条件下,考虑协变量后估计ROC曲线的两部贝叶斯模型。方法介绍两部贝叶斯模型,结合实例,筛选无金标准条件下ROC曲线的影响因素,考虑协变量影响后,估计ROC曲线。结果两部贝叶斯模型不仅可探讨协变量对疾病状态的影响,而且可探讨协变量对诊断试验结果的影响,同时可计算不同协变量取值条件下ROC曲线下面积。结论两部贝叶斯模型可有效地解决无金标准条件下,考虑协变量影响的ROC曲线估计问题。     

SROC曲线方法及其在唐氏综合征筛查试验评价中的应用

目的本文详细介绍了SROC曲线分析方法的原理及计算步骤，并应用于国外唐氏综合征筛查方案的评价与比较。方法运用SROC曲线分析方法，评价唐氏综合征筛查试验中的三种筛查方案的表现。结果依SROC曲线下的面积，在唐氏综合症的筛查试验中以母体血清学指标AFP与total-B HCG结合母体年龄别危险性的方法筛查效果最好。结论SROC曲线分析方法可以将同类研究的ROC曲线进行综合，可广泛应用于诊断与筛查试验的综合评价和比较，特别是基于meta分析的资料。     

两冠心病诊断试剂盒双正态ROC曲线下面积及其等效性或非劣效性检验

目的 探讨在双正态假定下,应用标准化差法进行定量资料ROC曲线下面积的估计及其等效性检验或非劣效性检验,比较两氧化低密度脂蛋白试剂盒在诊断冠心病中的价值.方法 从ROC曲线的定义出发,根据模型中参数的统计学意义,完成ROC曲线的构建、曲线下面积的估计,并利用标准化差结合等效性检验、非劣效性检验原理,进行参数检验,或在Bootstrap基础上利用可信区间法得到结论.结果 两试剂盒均显示氧化低密度脂蛋白在冠心病诊断中具有较高的准确性.从非劣效性检验的结果可以看出,CHN试剂盒在冠心病诊断上非劣于已经投入临床使用的SWZ试剂盒.结论 两试剂盒具有较高的临床推广价值,且具有较高性价比的CHN试剂盒在国内临床市场有较好的前景.同时为类似问题的解决提供了方法学参考.     

基于Matlab的受试者操作特征曲线分析

目的介绍新开发的受试者操作特征（Receiver Operating Characteristic,ROC）曲线分析软件。方法根据非参数法ROC曲线分析的基本原理,利用Matlab编写可视化的非参数法ROC曲线分析软件,对文献中的数据分别用本软件、SPSS及Analyse-it软件进行验证。最后利用本软件对两种技术检测的血清钠水平诊断洛矶山斑疹热（RMSF）的诊断作用进行评价和比较。结果本软件提供了非参数法ROC曲线分析的基本功能,能够直接输入或导入诊断试验数据,计算ROC曲线下面积并进行比较,并能保存ROC曲线操作点。与其他两个软件及文献对验证数据的ROC曲线分析结果完全相同,对应用实例进行了恰当的分析。结论本软件具有较完整的非参数法ROC曲线分析功能,在实际工作中具有一定的实用价值。     

Bootstrap法在ROC曲线下面积非劣效性检验中的应用及其SAS宏程序设计

[目的]探讨在双正态假定下,应用标准化差法在Bootstrap再抽样基础上利用SAS软件包编程进行定量资料ROC曲线下面积的估计及其非劣效性检验,以比较两氧化低密度脂蛋白试剂盒在冠心病诊断中的价值。[方法]从ROC曲线的定义出发,通过编写SAS宏程序完成ROC曲线下面积的估计,在Bootstrap基础上获得标准误的估计值,从而根据非劣效性检验原理进行参数检验,或利用可信区间法得到结论。[结果]两试剂盒均显示氧化低密度脂蛋白在冠心病诊断中具有较高的准确性。从非劣效性检验的结果可以看出,CHN试剂盒在冠心病诊断上非劣于已经投入临床使用的SWZ试剂盒。[结论]两试剂盒具有较高的临床推广价值,且具有较高性价比的CHN试剂盒在国内临床市场有较好的前景,同时为类似问题的解决提供了方法学参考。     

诊断试验准确性评价方法

着重介绍受试者工作特征（Receiver Operating Characteristic,ROC）曲线的理论,以及在诊断试验准确性评价中的应用。通过对五种乙型肝炎病毒表面抗原诊断试剂评价的实例,简要说明ROC曲线下面积（Area Under Curve,AUC）、部分ROCAUC和固定特异度时灵敏度的计算方法及其意义,以及这些指标相互比较的Bootstrap方法。     

配对设计生存资料的统计分析及其在临床试验中的应用

目的 介绍配对设计生存资料的统计分析方法及其在临床试验中的应用。方法 运用基于协方差矩阵稳缝估计的Cox回归模型，通过调用STATA统计软件包中stcox命令中的cluster选项。对配对设计生存资料进行统计分析。并用治疗烧伤临床试验中的实例比较了运用和不运用配对设计生存资料的统计分析方法的两种结果。结果 实例分析表明运用配对设计生存资料的统计分析方法时，可得出治疗组的创面愈合时间小于安慰剂组的结论，而不运用配对设计生存资料的统计分析方法时，其结论为治疗组和安慰剂组创面愈合时间的差异无统计学意义。结论 在对配对设计的生存资料进行统计分析时．应选用配对设计生存资料的统计分析方法，否则将降低检验的效率，可能得出不正确的结沦。     

A non-inferiority test for diagnostic accuracy based on the paired partial areas under ROC curves

Non-inferiority is a reasonable approach to assessing the diagnostic accuracy of a new diagnostic test if it provides an easier administration or reduces the cost. The area under the receiver operating characteristic (ROC) curve is one of the common measures for the overall diagnostic accuracy. However, it may not differentiate the various shapes of the ROC curves with different diagnostic significances. The partial area under the ROC curve (PAUROC) may present an alternative that can provide additional and complimentary information for some diagnostic tests which require false-positive rate that does not exceed a certain level. Non-parametric and maximum likelihood methods can be used for the non-inferiority tests based on the difference in paired PAUROCs. However, their performance has not been investigated in finite samples. We propose to use the concept of generalized p-value to construct a non-inferiority test for diagnostic accuracy based on the difference in paired PAUROCs. Simulation results show that the proposed non-inferiority test not only adequately controls the size at the nominal level but also is uniformly more powerful than the non-parametric methods. The proposed method is illustrated with a numerical example using published data.     

Bayesian bootstrap estimation of ROC curve

Receiver operating characteristic (ROC) curve is widely applied in measuring discriminatory ability of diagnostic or prognostic tests. This makes the ROC analysis one of the most active research areas in medical statistics. Many parametric and semiparametric estimation methods have been proposed for estimating the ROC curve and its functionals. In this paper, we propose the Bayesian bootstrap (BB), a fully nonparametric estimation method, for the ROC curve and its functionals, such as the area under the curve (AUC). The BB method offers a bandwidth-free smoothing approach to the empirical estimate, and gives credible bounds. The accuracy of the estimate of the ROC curve in the simulation studies is examined by the integrated absolute error. In comparison with other existing curve estimation methods, the BB method performs well in terms of accuracy, robustness and simplicity. We also propose a procedure based on the BB approach to test the binormality assumption.     

Interval estimation for the difference in paired areas under the ROC curves in the absence of a gold standard test

Receiver operating characteristic (ROC) curves can be used to assess the accuracy of tests measured on ordinal or continuous scales. The most commonly used measure for the overall diagnostic accuracy of diagnostic tests is the area under the ROC curve (AUC). A gold standard (GS) test on the true disease status is required to estimate the AUC. However, a GS test may sometimes be too expensive or infeasible. Therefore, in many medical research studies, the true disease status of the subjects may remain unknown. Under the normality assumption on test results from each disease group of subjects, using the expectation‐maximization (EM) algorithm in conjunction with a bootstrap method, we propose a maximum likelihood‐based procedure for the construction of confidence intervals for the difference in paired AUCs in the absence of a GS test. Simulation results show that the proposed interval estimation procedure yields satisfactory coverage probabilities and interval lengths. The proposed method is illustrated with two examples. Copyright © 2009 John Wiley & Sons, Ltd.     

Combining dependent tests to compare the diagnostic accuracies--a non-parametric approach

In this paper, we propose a non-parametric approach for comparing diagnostic accuracies in multi-reader receiver operating characteristic (ROC) studies. The approach constructs a test from each reader by extending the conventional non-parametric method and then combines all the individual test statistics to draw an overall conclusion on the relative accuracies of different diagnostic tests. The method can handle both continuous and ordinal data. Compared to the existing non-parametric methods, the method is robust and effectively deals with the possible heterogeneity among readers. It can also be applied to the analysis of correlated ROC studies. The method is applied to a real example and its finite sample performance is examined through simulation studies.     

On the exact interval estimation for the difference in paired areas under the ROC curves

An important measure for comparison of accuracy between two diagnostic procedures is the difference in paired areas under the receiver operating characteristic (ROC) curves. Non-parametric and maximum likelihood methods have been proposed for interval estimation for the difference in paired areas under ROC curves. However, these two methods are asymptotic procedures and their performance in finite sample sizes has not been thoroughly investigated. We propose to use the concept of generalized pivotal quantities (GPQs) to construct an exact confidence interval for the difference in paired areas under ROC curves. A simulation study is conducted to empirically investigate the probability coverage and expected length of the three methods for various combinations of sample sizes, values of the area under the ROC curve and correlations. Simulation results demonstrate that the exact confidence interval based on the concept of GPQs provides not only sufficient probability coverage but also reasonable expected length. Numerical examples using published data sets illustrate the proposed method.     

An evaluation of methods for estimating the area under the receiver operating characteristic (ROC) curve

The area under the receiver operating characteristic (ROC) curve serves as one means for evaluating the performance of diagnostic and predictive test systems. The most commonly used method for estimating the area under an ROC curve utilizes the maximum-likelihood-estimation technique, and a nonparametric method to calculate the area under an ROC curve was recently described. We compared the performance of these two methods. The results for the area under the ROC curve and the standard error of the estimate as calculated by each of the two methods exhibited high correlation. Generally, the nonparametric method yields lower area estimates than the maximum-likelihood-estimation technique. However, these differences generally were small, particularly with ROC curves derived from five or more cutoff points. Consistent results of hypothesis testing of the significance of differences between two ROC curves will be similar, regardless of which method is used, as long as one uses the same estimation technique on the two curves and as long as the two ROC curves being compared are of similar shape.     

Efficiency of study designs in diagnostic randomized clinical trials

From the patients’ management perspective, a good diagnostic test should contribute to both reflecting the true disease status and improving clinical outcomes. The diagnostic randomized clinical trial is designed to combine both diagnostic tests and therapeutic interventions. Evaluation of diagnostic tests is carried out with therapeutic outcomes as the primary endpoint rather than test accuracy. We lay out the probability framework for evaluating such trials. We compare two commonly referred designs—the two‐arm design and the paired design—in a formal statistical hypothesis testing setup and identify the causal connection between the two tests. The paired design is shown to be more efficient than the two‐arm design. The efficiency gains vary depending on the discordant rates of test results. We derive sample size formulas for both binary and continuous endpoints. We derive estimation of important quantities under the paired design and also conduct simulation studies to verify the theoretical results. We illustrate the method with an example of designing a randomized study on preoperative staging of bladder cancer. Copyright © 2012 John Wiley & Sons, Ltd.