具有协变量或干扰因素的诊断试验数据的ROC分析

目的 探讨具有协变量或干扰因素的诊断试验的评价问题。建立相应的统计模型及分析方法。方法 基于ROC分析给出变量ROC模型，采用有序logistic连续函数，利用SAS软件进行参数估计，得到有协为量情况下的ROC曲线方程及曲线下面积。文中结合检测动脉硬化的数据，说明了其用法。结果 采用文中给出的方法，可以有效地扣除协变量的影响，准确地评价诊断的作用。结论 本文提供的方法能够有效地解决诊断试验中具有干扰因素影响的问题。     

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.     

重复测量诊断试验的ROC曲线广义线性混合效应模型北大核心CSCD

目的针对重复测量诊断数据,为同时考虑协变量对诊断试验准确性评价的影响,度量重复测量数据间的相关性,本文探索新的ROC曲线的建模方法。方法通过广义线性混合效应模型对ROC曲线进行模拟,并采用贝叶斯参数估计方法,利用Win BUGS软件予以实现,进而计算不同协变量取值下的ROC曲线下面积(AUC)以对诊断试验结果进行评价。结果实例数据分析结果表明,基于广义线性混合效应模型的ROC曲线建模方法可以有效地刻画重复测量诊断试验数据,给出更有解释意义的回归参数,提供临床分析的参考依据。结论基于广义线性混合效应的ROC曲线模型在解决重复测量诊断试验的准确度评价问题起着至关重要的作用。     

logistic回归模型在ROC分析中的应用

目的探讨logistic回归模型在有协变量或多指标联合诊断试验ROC分析中的应用。方法根据疾病状态建立logistic回归模型,通过形成的预测概率或联合预测因子为分析指标,并结合非参数模型和双正态模型建立ROC曲线。结果通过实例阐述了整个分析过程,并说明了该试剂盒的有效性,同时利用两种模型得到了一致的结果。结论ROC分析中结合logistic回归模型简单有效,尤其适用于有协变量或多指标联合诊断试验的分析评价。     

重复测量诊断试验的ROC曲线广义线性混合效应模型

目的针对重复测量诊断数据,为同时考虑协变量对诊断试验准确性评价的影响,度量重复测量数据间的相关性,本文探索新的ROC曲线的建模方法。方法通过广义线性混合效应模型对ROC曲线进行模拟,并采用贝叶斯参数估计方法,利用Win BUGS软件予以实现,进而计算不同协变量取值下的ROC曲线下面积(AUC)以对诊断试验结果进行评价。结果实例数据分析结果表明,基于广义线性混合效应模型的ROC曲线建模方法可以有效地刻画重复测量诊断试验数据,给出更有解释意义的回归参数,提供临床分析的参考依据。结论基于广义线性混合效应的ROC曲线模型在解决重复测量诊断试验的准确度评价问题起着至关重要的作用。     

临床试验评价的ROC分析方法

目的探讨新药临床试验效果的评价问题,给出一种新的统计分析方法。方法基于ROC分析给出多变量ROC模型,采用有序logit联系函数,利用SAS软件进行参数估计,得到有协变量及交互作用情况下的ROC曲线方程及曲线下面积。结果采用文中给出的方法,可以有效地扣除协变量的影响,用ROC曲线直观地评价药物之间的差别和作用。结论本文提供的方法能够更有效地对临床试验做出客观和准确的评价。     

四种与冠心病相关指标联合诊断冠心病价值评价

目的：探讨klgistic多指标联合诊断试验ROC分析中的应用，评价4种与冠心病发病有关指标在冠心病诊断及联合诊断中的效果。方法：根据疾病状态建立klgistic回归模型，通过形成的预测概率或联合预测因子为分析指标，并结合双正态模型建立ROC曲线。结果：通过实例阐述了整个分析过程，并说明了指标对诊断冠心病的有效性，确定了基于联合诊断的晟佳工作点。结论：ROC分析中结合logistic回归模型简单有效，尤其适用于有协变量或多指标联合诊断试验的分析评价。     

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。     

不明位置妊娠Logistic回归超声诊断模型的初步研究

目的:通过经阴道超声(TVS)检测不明位置妊娠(PUL)患者的子宫内膜,建立鉴别诊断的多变量回归模型,并评价该模型的临床应用价值。方法:分析早期PUL患者的子宫内膜二维声像图特征,并对年龄、β-HCG、停经时间等进行分析,采用多变量Logistic回归法及ROC曲线筛选出有意义的诊断指标,建立PUL患者的多变量回归模型。结果:建立了三个预测PUL妊娠结局的Logistic回归模型,分别对宫内早孕(MIUP)、宫内早孕流产(MIUPM)和早期异位妊娠(MEP)进行评估,ROC曲线下面积(AUC)分别为0.941、0.886、0.891,预测模型循证试验的诊断指标均优于各自回归模型中任何单一变量。结论:本研究所建立的多变量预测模型能更早期、更有效地预测PUL患者的妊娠状态,对早期PUL患者妊娠结局的预测有较大的临床价值。     

时点/动态ROC曲线法在生存模型预后评价中的应用

目的探讨时点/动态ROC曲线法在生存模型的预后评价中的应用。方法介绍时点/动态ROC曲线法的基本原理,以Cox比例风险模型为例,阐述如何采用Cox模型输出结果获取时点灵敏度与动态特异度及时依ROC曲线,通过对各时点曲线下的面积进行加权平均得到整个随访期模型的总体预测准确度。结果时点/动态的ROC曲线法是以时刻风险集中的病例与对照的分类为基础,该定义与风险模型的意义相吻合。同时,可以扩展到协变量不满足比例风险的生存模型预测准确度的评价。结论时点/动态的ROC曲线法可以通过不同时点下风险集的变化情况动态地观察模型预测准确度的瞬时变化情况,可作为生存分析中决定系数这一指标更好的替代方法。     

Semiparametric ROC surfaces for continuous diagnostic tests based on two test measurements

We propose a semiparametric method for estimating ROC surfaces for continuous diagnostic tests based on two test measurements. Such a three‐class diagnostic problem based on two test measurements arises naturally from some DNA amplification‐related diagnostic scenarios. Simulation results show that our proposed semiparametric ROC surface estimator is more efficient than the nonparametric counterpart and is quite comparable with the parametric counterpart when model assumption of the data is correctly specified for the parametric method. Moreover, when the parametric model assumption is not true, our proposed semiparametric method is superior to both nonparametric and parametric methods. Some discussions and a simulated example along with R programs for implementation of the proposed method are also provided. Copyright © 2009 John Wiley & Sons, Ltd.     

Small area estimation of receiver operating characteristic curves for ordinal data under stochastic ordering

There has been a recent increase in the diagnosis of diseases through radiographic images such as x-rays, magnetic resonance imaging, and computed tomography. The outcome of a radiological diagnostic test is often in the form of discrete ordinal data, and we usually summarize the performance of the diagnostic test using the receiver operating characteristic (ROC) curve and the area under the curve (AUC). The ROC curve will be concave and called proper when the outcomes of the diagnostic test in the actually positive subjects are higher than in the actually negative subjects. The diagnostic test for disease detection is clinically useful when a ROC curve is proper. In this study, we develop a hierarchical Bayesian model to estimate the proper ROC curve and AUC using stochastic ordering in several domains when the outcome of the diagnostic test is discrete ordinal data and compare it with the model without stochastic ordering. The model without stochastic ordering can estimate the improper ROC curve with a nonconcave shape or a hook when the true ROC curve of the population is a proper ROC curve. Therefore, the model with stochastic ordering is preferable over the model without stochastic ordering to estimate the proper ROC curve with clinical usefulness for ordinal data.     

ROC曲线下面积的ML估计与假设检验

目的 探讨诊断试验中配对设计资料的ROC分析方法。方法 在双正态模型基础下应用ML估计方法计算ROC曲线下面积，正态近似法估计面积的可信区间及假设检验。结果 由迭代法进行参数估计，得到ROC曲线下的面积、面积的标准误及置信区间，可计算出面积比较的U检验统计量。结论 可用于配对设计的诊断试验的比较和评价，包括对连续性和等级分类资料的处理。     

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.     

A general regression methodology for ROC curve estimation

A method for applying generalized ordinal regression models to categorical rating data to estimate and analyze receiver operating characteristic (ROC) curves is presented. These models permit parsimonious adjustment of ROC curve parameters for relevant covariates through two regression equations that correspond to location and scale. Particular shapes of ROC curves are interpreted in relation to the kind of covariates included in the two regressions. The model is shown to be flexible because it is not restricted to the assumption of binormality that is commonly employed in smoothed ROC curve estimation, although the binormal model is one particular form of the more general model. The new method provides a mechanism for pinpointing the effect that interobserver variability has on the ROC curve. It also allows for the adjustment of ROC curves for temporal variation and case mix, and provides a way to assess the incremental diagnostic value of a test. The new methodology is recommended because it substantially improves the ability to assess diagnostic tests using ROC curves.     

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.     

Modeling continuous diagnostic test data using approximate Dirichlet process distributions

There is now a large literature on the analysis of diagnostic test data. In the absence of a gold standard test, latent class analysis is most often used to estimate the prevalence of the condition of interest and the properties of the diagnostic tests. When test results are measured on a continuous scale, both parametric and nonparametric models have been proposed. Parametric methods such as the commonly used bi-normal model may not fit the data well; nonparametric methods developed to date have been relatively complex to apply in practice, and their properties have not been carefully evaluated in the diagnostic testing context. In this paper, we propose a simple yet flexible Bayesian nonparametric model which approximates a Dirichlet process for continuous data. We compare results from the nonparametric model with those from the bi-normal model via simulations, investigating both how much is lost in using a nonparametric model when the bi-normal model is correct and how much can be gained in using a nonparametric model when normality does not hold. We also carefully investigate the trade-offs that occur between flexibility and identifiability of the model as different Dirichlet process prior distributions are used. Motivated by an application to tuberculosis clustering, we extend our nonparametric model to accommodate two additional dichotomous tests and proceed to analyze these data using both the continuous test alone as well as all three tests together.     

Receiver operator characteristic (ROC) analysis without truth

Receiver operator characteristic (ROC) analysis, the preferred method of evaluating diagnostic imaging tests, requires an independent assessment of the true state of disease, which can be difficult to obtain and is often of questionable accuracy. A new method of analysis is described which does not require independent truth data and which can be used when several accurate tests are being compared. This method uses correlative information to estimate the underlying model of multivariate normal distributions of disease-positive and disease-negative patients. The method is shown to give results equivalent to conventional ROC analysis in a comparison of computed tomography, radionuclide scintigraphy, and magnetic resonance imaging for liver metastasis. When independent truth is available, the method can be extended to incorporate truth data or to evaluate the consistency of the truth data with the imaging data.