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。     

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。     

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。     

无金标准条件下诊断试验评价贝叶斯相关模型构建及应用

目的探讨无金标准条件下诊断试验贝叶斯相关模型构建方法及应用条件。方法通过分析具有潜在真值的无金标准诊断试验评价模型,构建两个试验相关条件下的似然函数;利用共轭分布原理,构建灵敏度、特异度、患病率的先验分布;使用WinBUGS软件计算后验参数。通过234602名无偿献血员抗-HIV检测结果说明贝叶斯相关模型的应用。结果构建了无金标准时两次ELISA法检测抗-HIV的贝叶斯相关模型,发现两次ELISA的灵敏度相关系数为0.30,特异度相关系数为0.74;两次试验的联合灵敏度较单个试剂增高(P<0.05),特异度较单个试剂降低(P<0.05),但特异度降低的幅度明显小于灵敏度增高的幅度。结论应用贝叶斯相关模型可合理评价无金标准时联合试验的灵敏度和特异度。     

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。     

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。     

无金标准诊断试验灵敏度和特异度的贝叶斯估计方法

目的 探讨在无金标准情况下诊断试验灵敏度和特异度的贝叶斯估计方法,并通过模拟实验验证此方法的有效性和适用性.方法 应用贝叶斯原理,结合一个或者两个对照诊断试验,采用对照诊断试验与待测诊断试验诊断结果相互独立或者存在相关模型;和两个对照诊断试验诊断结果存在相关,并且同时与待测诊断试验结果相互独立模型;以及一个对照诊断试验与待测诊断试验诊断结果存在相关,并且同时与另一个对照诊断试验诊断结果相互独立模型,对一种新的诊断方法的灵敏度、特异度进行估计.结果 模拟试验表明在先验信息相对准确的条件下,所给出的几种不同模型均能较准确的估计待测诊断方法的灵敏度和特异度,估测参数范围包括设定真值,并且估测参数均值基本与真实情况相同;此外,模拟实验结果表明,利用两个对照诊断试验模型估计参数的估计效果优于使用一个对照诊断试验模型时的参数估计效果.结论 本文介绍的贝叶斯方法能够有效地应用于无金标准诊断试验的评价,从而解决了无金标准诊断试验无法评价的难题.     

基于贝叶斯网络模型对重症手足口病预测变量的筛选研究

目的通过构建贝叶斯网络模型,来对重症手足口病的预测变量进行相关筛选以及探讨相应变量之间的依赖关系。方法收集郑州市某医院感染科2016年4月到2017年10月收治的手足口病患儿资料。选用SPSS Statistics 25.0进行统计学方面的分析,SPSS Modeler 18.0软件则用于构建TAN贝叶斯网络和Logistic回归模型,以Logistic回归模型作为对比,并对两个模型的整体拟合性能进行评价。结果最终筛选出7个重要变量,在所有预测变量中,对输出变量分组影响最大的前三个分别为:病原学结果、肢体抖动和中性细胞比率。TAN贝叶斯网络模型总体预测正确率为83.32%,ROC曲线下面积0.815,灵敏度71.87%,特异度为91.28%。结论 TAN贝叶斯网络模型在筛选重症手足口病预测变量方面具有较好的应用价值,同时模型的预测性能也较好。     

非劣效性临床试验中两组率差值的协变量调整方法

目的 解决新药非劣效性临床试验评价中两组率差值的协变量调整问题.方法 利用ROC曲线与两组率差值的对应关系,通过ROC分析间接得到调整协变量之后两组率之差的估计值及标准误.结果 给出了一种新的非参数分层分析方法,如果协变量对有效率有影响,可以通过调整分析得到更准确的检验结果.结论 本文提供的方法能够更直观、有效地对新药临床试验的数据进行非劣效性评价.     

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.     

Bayesian ROC curve estimation under verification bias

Receiver operating characteristic (ROC) curve has been widely used in medical science for its ability to measure the accuracy of diagnostic tests under the gold standard. However, in a complicated medical practice, a gold standard test can be invasive, expensive, and its result may not always be available for all the subjects under study. Thus, a gold standard test is implemented only when it is necessary and possible. This leads to the so‐called ‘verification bias’, meaning that subjects with verified disease status (also called label) are not selected in a completely random fashion. In this paper, we propose a new Bayesian approach for estimating an ROC curve based on continuous data following the popular semiparametric binormal model in the presence of verification bias. By using a rank‐based likelihood, and following Gibbs sampling techniques, we compute the posterior distribution of the binormal parameters intercept and slope, as well as the area under the curve by imputing the missing labels within Markov Chain Monte‐Carlo iterations. Consistency of the resulting posterior under mild conditions is also established. We compare the new method with other comparable methods and conclude that our estimator performs well in terms of accuracy. Copyright © 2014 John Wiley & Sons, Ltd.     

Bayesian semi-parametric ROC analysis

This paper describes a semi-parametric Bayesian approach for estimating receiver operating characteristic (ROC) curves based on mixtures of Dirichlet process priors (MDP). We address difficulties in modelling the underlying distribution of screening scores due to non-normality that may lead to incorrect choices of diagnostic cut-offs and unreliable estimates of prevalence of the disease. MDP is a robust tool for modelling non-standard diagnostic distributions associated with imperfect classification of an underlying diseased population, for example, when a diagnostic test is not a gold standard. For posterior computations, we propose an efficient Gibbs sampling framework based on a finite-dimensional approximation to MDP. We show, using both simulated and real data sets, that MDP modelling for ROC curve estimation closely parallels the frequentist kernel density estimation (KDE) approach.     

Bayesian nonparametric estimation of ROC surface under verification bias

The receiver operating characteristic (ROC) surface, as a generalization of the ROC curve, has been widely used to assess the accuracy of a diagnostic test for three categories. A common problem is verification bias, referring to the situation where not all subjects have their true classes verified. In this paper, we consider the problem of estimating the ROC surface under verification bias. We adopt a Bayesian nonparametric approach by directly modeling the underlying distributions of the three categories by Dirichlet process mixture priors. We propose a robust computing algorithm by only imposing a missing at random assumption for the verification process but no assumption on the distributions. The method can also accommodate covariates information in estimating the ROC surface, which can lead to a more comprehensive understanding of the diagnostic accuracy. It can be adapted and hugely simplified in the case where there is no verification bias, and very fast computation is possible through the Bayesian bootstrap process. The proposed method is compared with other commonly used methods by extensive simulations. We find that the proposed method generally outperforms other approaches. Applying the method to two real datasets, the key findings are as follows: (1) human epididymis protein 4 has a slightly better diagnosis ability compared to CA125 in discriminating healthy, early stage, and late stage patients of epithelial ovarian cancer. (2) Serum albumin has a prognostic ability in distinguishing different stages of hepatocellular carcinoma.     

Bayesian multivariate hierarchical transformation models for ROC analysis

A Bayesian multivariate hierarchical transformation model (BMHTM) is developed for receiver operating characteristic (ROC) curve analysis based on clustered continuous diagnostic outcome data with covariates. Two special features of this model are that it incorporates non-linear monotone transformations of the outcomes and that multiple correlated outcomes may be analysed. The mean, variance, and transformation components are all modelled parametrically, enabling a wide range of inferences. The general framework is illustrated by focusing on two problems: (1) analysis of the diagnostic accuracy of a covariate-dependent univariate test outcome requiring a Box-Cox transformation within each cluster to map the test outcomes to a common family of distributions; (2) development of an optimal composite diagnostic test using multivariate clustered outcome data. In the second problem, the composite test is estimated using discriminant function analysis and compared to the test derived from logistic regression analysis where the gold standard is a binary outcome. The proposed methodology is illustrated on prostate cancer biopsy data from a multi-centre clinical trial.     

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。     

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。     

Bayesian estimation of the receiver operating characteristic curve for a diagnostic test with a limit of detection in the absence of a gold standard

The receiver operating characteristic (ROC) curve is commonly used for evaluating the discriminatory ability of a biomarker. Measurements for a diagnostic test may be subject to an analytic limit of detection leading to immeasurable or unreportable test results. Ignoring the scores that are beyond the limit of detection of a test leads to a biased assessment of its discriminatory ability, as reflected by indices such as the associated area under the curve (AUC). We propose a Bayesian approach for the estimation of the ROC curve and its AUC for a test with a limit of detection in the absence of gold standard based on assumptions of normally and gamma‐distributed data. The methods are evaluated in simulation studies, and a truncated gamma model with a point mass is used to evaluate quantitative real‐time polymerase chain reaction data for bovine Johne's disease (paratuberculosis). Simulations indicated that estimates of diagnostic accuracy and AUC were good even for relatively small sample sizes (n=200). Exceptions were when there was a high per cent of unquantifiable results (60 per cent) or when AUC was ?0.6, which indicated a marked overlap between the outcomes in infected and non‐infected populations. Copyright © 2010 John Wiley & Sons, Ltd.