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1.
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   

2.
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   

3.
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。  相似文献   

4.
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   

5.
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   

6.
侯艳  李康 《中国卫生统计》2012,29(2):196-198,201
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.  相似文献   

7.
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。  相似文献   

8.
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   

9.
诊断试验ROC参数估计双正态样本量估计方法探讨   总被引:2,自引:0,他引:2  
目的 探讨ROC双正态样本量估计方法的准确性。方法 通过Monte Carlo方法对ROC双正态样本量估计法进行评价与修正。结果 根据模拟试验结果得到双正态样本量估计法的校正公式及修正曲线。结论 采用文中给出的样本量调整方法。可以有效地进行样本量估计。达到诊断试验评价的要求。  相似文献   

10.
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   

11.
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   

12.
医学诊断试验评价的ROC分析—重复测量诊断数据的ROC曲线   总被引:7,自引:0,他引:7  
目的 探讨诊断试验中重复有序分类测量数据的ROC分析方法。方法 在有序模型基础上构造相应的ROC模型,然后选用累积logistic模型作为联系函数,采用GEE方法进行参数估计。结果 通过累积logistic模型的参数估计,能够得到ROC曲线方程、ROC曲线下面积及标准误。结论 上术方法影像诊断试验中常见的重复测量数据进行有效地分析与处理。  相似文献   

13.
ROC methodology within a monitoring framework   总被引:1,自引:0,他引:1  
Receiver operating characteristic (ROC) methodology is widely used to evaluate and compare diagnostic tests. Generally, each diagnostic test is applied once to each subject in a population and the results, reported on a continuous scale, are used to construct the ROC curve. We extend the standard method to accommodate a framework in which the diagnostic test is repeated over time to monitor for occurrence of an event. Unlike the usual situation in which event status is static, the problem we address involves event status that is not constant over the monitoring period. Subjects generally are classified as non-events, or controls, until they experience events that convert them to cases. Viewing the data as incomplete discrete failure time data with time-varying covariates, potentially useful diagnostic markers can be related appropriately in time with the true condition and varying amounts of information per individual can be taken into account. The ROC curve provides an assessment of the performance of the test in combination with the schedule of testing. Within this framework, a computational simplification is introduced to calculate variances and covariances for the areas under the ROC curves. Periodic monitoring for reperfusion following thrombolytic treatment for acute myocardial infarction provides a detailed example, whereby the lengths of the testing interval combined with different diagnostic markers are compared.  相似文献   

14.
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   

15.
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   

16.
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   

17.
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   

18.
The authors provide a simple calculation for the unbiased estimation of the area under the ROC curve for a binary diagnostic test or a continuously valued test result that is effectively used in a binary way. The formula described can be used to interpret the discriminative ability of a diagnostic test.  相似文献   

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