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1.
具有协变量或干扰因素的诊断试验数据的ROC分析   总被引:7,自引:3,他引:4  
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。  相似文献   

2.
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。  相似文献   

3.
基于贝叶斯估计的诊断试验ROC曲线回归模型   总被引:1,自引:0,他引:1  
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.  相似文献   

4.
连续变量诊断试验数据的ROC分析   总被引:2,自引:2,他引:2  
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。  相似文献   

5.
ROC曲线下面积的ML估计与假设检验   总被引:5,自引:0,他引:5  
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。  相似文献   

6.
临床试验评价的ROC分析方法   总被引:1,自引:1,他引:1  
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。  相似文献   

7.
logistic回归模型在ROC分析中的应用   总被引:5,自引:0,他引:5  
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。  相似文献   

8.
目的应用logistic回归和ROC曲线探讨甲状腺球蛋白(TG)及甲状腺球蛋白抗体(ATG)对甲状腺癌和良性甲状腺结节鉴别诊断的价值。方法以病理学检查结果作为诊断金标准,采用电化学发光法测定147例甲状腺癌患者、220例良性甲状腺结节患者及150例健康者的血清TG和ATG,通过ROC曲线和logistic回归评价TG和ATG单项及两项联合检测结果。结果通过ROC曲线评价TG和ATG,其曲线下面积(AUC)分别为0.704和0.710。TG和ATG两项指标联合应用的诊断效率最好,其值为0.780。结论通过测定血清TG和ATG,并构建logistic回归模型和应用ROC曲线分析,能方便、有效地评价TG和ATG联合检测结果在甲状腺癌和良性甲状腺结节的鉴别诊断价值,有助于术前区分结节的性质,从而指导临床采取合理有效的治疗手段。  相似文献   

9.
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.  相似文献   

10.
目的:探讨多水平模型理论在有序分类重复测量数据具有非独立性或误差分布于多个层次时的应用。方法:阐述了多水平模型的构造方法,及模型估计的实现,结果:对有序分类重复测量数据实例拟合方差成分模型及随机系数模型,并得到合理的解释。结论:采用多水平模型分析有序分类的重复测量数据。效果较好,建议采用RIGLS计算,参数估计采用PQL估计。  相似文献   

11.
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.  相似文献   

12.
Clinicians and health service researchers are frequently interested in predicting patient-specific probabilities of adverse events (e.g. death, disease recurrence, post-operative complications, hospital readmission). There is an increasing interest in the use of classification and regression trees (CART) for predicting outcomes in clinical studies. We compared the predictive accuracy of logistic regression with that of regression trees for predicting mortality after hospitalization with an acute myocardial infarction (AMI). We also examined the predictive ability of two other types of data-driven models: generalized additive models (GAMs) and multivariate adaptive regression splines (MARS). We used data on 9484 patients admitted to hospital with an AMI in Ontario. We used repeated split-sample validation: the data were randomly divided into derivation and validation samples. Predictive models were estimated using the derivation sample and the predictive accuracy of the resultant model was assessed using the area under the receiver operating characteristic (ROC) curve in the validation sample. This process was repeated 1000 times-the initial data set was randomly divided into derivation and validation samples 1000 times, and the predictive accuracy of each method was assessed each time. The mean ROC curve area for the regression tree models in the 1000 derivation samples was 0.762, while the mean ROC curve area of a simple logistic regression model was 0.845. The mean ROC curve areas for the other methods ranged from a low of 0.831 to a high of 0.851. Our study shows that regression trees do not perform as well as logistic regression for predicting mortality following AMI. However, the logistic regression model had performance comparable to that of more flexible, data-driven models such as GAMs and MARS.  相似文献   

13.
A general regression methodology for ROC curve estimation   总被引:5,自引:0,他引:5  
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.  相似文献   

14.
SPSS 中的 ROC 分析用于检验/诊断方法的评价   总被引:13,自引:2,他引:11  
[目的]介绍如何用SPSS软件包中新颖的ROC分析法,来对一种或几种检验/诊断方法进行科学评价。[方法]回顾目前文献中的存在问题;对四格表排列、数据库录入以及ROC命令默认设置提出应用或改变建议;举例说明定性、定量资料的RoC分析方法;介绍2或3种诊断方法诊断效能比较的RoC分析方法与结果;RoC分析结果与列联表分析、logistic比回归及判别分析结果相印证。[结果]按本文建议,ROC分所能方便地对一种或几种检验/诊断方法进行科学评价,算出其敏感性与特异性等6项指标,且与其他3种分析方法统一、对应。[结论]ROC曲线能将诊断方法的敏感性与特异性结合起来进行分析,而不是仅侧重于其敏感性或特异性,又能表示为“曲线下的面积越大,其诊断试验效果越好”,既全面又直观,又与其他统计分析方法结果对应、统一,值得在检验/诊断方法科学评价中广泛应用。  相似文献   

15.
比例优势模型实现ROC分析的方法及其应用前景分析   总被引:1,自引:1,他引:1  
目的 探讨比例优势模型在ROC分析中的应用前景。方法 比较比例优势模型与双正态模型等经典方法所计算的ROC曲线下面积及其标准误;采用灵敏度残差平方和与决定系数两个指标评价参数模型的拟合优度。结果 在一般情况下,由比例优势模型所得到的Roe曲线指标结果与经典方法很接近;对于有序分类资料和连续型资料,该模型的拟合效果均较好;但由于该模型获得的Roe曲线形状单一,有些情况下该模型的拟合不理想。结论 与经典的方法相比,比例优势模型有其自身的特点,实际应用时应慎重做出选择。  相似文献   

16.
Receiver operating characteristic (ROC) curves provides a method for evaluating the performance of a diagnostic test. These curves represent the true positive ratio, that is, the true positives among those affected by the disease, as a function of the false positive ratio, that is, the false positives among the healthy, corresponding to each possible value of the diagnostic variable. When the diagnostic variable is continuous, the corresponding ROC curve is also continuous. However, estimation of such curve through the analysis of sample data yields a step-line, unless some assumption is made on the underlying distribution of the considered variable. Since the actual distribution of the diagnostic test is seldom known, it is difficult to select an appropriate distribution for practical use. Data transformation may offer a solution but also may introduce a distortion on the evaluation of the diagnostic test. In this paper we show that the distribution family known as the S-distribution can be used to solve this problem. The S-distribution is defined as a differential equation in which the dependent variable is the cumulative. This special form provides a highly flexible family of distributions that can be used as models for unknown distributions. It has been shown that classical statistical distributions can be represented accurately as S-distributions and that they occur in a definite subspace of the parameter space corresponding to the whole S-distribution family. Consequently, many other distributional forms that do not correspond to known distributions are provided by the S-distribution. This property can be used to model observed data for unknown distributions and is very useful in constructing parametric ROC curves in those cases. After fitting an S-distribution to the observed samples of diseased and healthy populations, ROC curve computation is straightforward. A ROC curve can be considered as the solution of a differential equation in which the dependent variable is the ratio of true positives and the independent variable is the ratio of false positives. This equation can be easily obtained from the S-distributions fitted to observed data. Using these results, we can compute pointwise confidence bands for the ROC curve and the corresponding area under the curve. We shall compare this approach with the empirical and the binormal methods for estimating a ROC curve to show that the S-distribution based method is a useful parametric procedure.  相似文献   

17.
The authors present a method to combine several independent studies of the same (continuous or semiquantitative) diagnostic test, where each study reports a complete ROC curve; a plot of the true-positive rate or sensitivity against the false-positive rate or one minus the specificity. The result of the analysis is a pooled ROC curve, with a confidence band, as opposed to earlier proposals that result in a pooled area under the ROC curve. The analysis is based on a two-parameter model for the ROC curve that can be estimated for each individual curve. The parameters are then pooled with a bivariate random-effects meta-analytic method, and a curve can be drawn from the pooled parameters. The authors propose to use a model that specifies a linear relation between the logistic transformations of sensitivity and one minus specificity. Specifically, they define V = In(sensitivity/(1 - sensitivity)) and U = In((1 - specificity)/specificity), and then D = V - U, S = V + U. The model is defined as D = alpha + betaS. The parameters alpha and beta are estimated using weighted linear regression with bootstrapping to get the standard errors, or using maximum likelihood. The authors show how the procedure works with continuous test data and with categorical test data.  相似文献   

18.
While estimating odds ratios (ORs) in the context of dose levels of conjugated oestrogen exposure and development of endometrial cancer, the categories formed by the levels of the exposure are ordinal in nature. In the literature, the binary logistic model is used for estimating OR for each category relative to the baseline category. We describe the use of two ordinal logistic models, the cumulative logit and continuation-ratio logit models, to estimate the ORs for the matched pairs case-control data set of the Los Angeles endometrial cancer study. A test for equality of the cumulative ORs across the exposure levels is proposed. The test statistic follows asymptotically the chi-square distribution.  相似文献   

19.
The receiver operating characteristic (ROC) curve is a statistical tool for evaluating the accuracy of diagnostic tests. Investigators often compare the validity of two tests based on the estimated areas under the respective ROC curves. However, the traditional way of comparing entire areas under two ROC curves is not sensitive when two ROC curves cross each other. Also, there are some cutpoints on the ROC curves that are not considered in practice because their corresponding sensitivities or specificities are unacceptable. For the purpose of comparing the partial area under the curve (AUC) within a specific range of specificity for two correlated ROC curves, a non-parametric method based on Mann-Whitney U-statistics has been developed. The estimation of AUC along with its estimated variance and covariance is simplified by a method of grouping the observations according to their cutpoint values. The method is used to evaluate alternative logistic regression models that predict whether a subject has incident breast cancer based on information in Medicare claims data.  相似文献   

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