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1.
  总被引:1,自引:0,他引:1  
Pepe MS  Cai T  Longton G 《Biometrics》2006,62(1):221-229
No single biomarker for cancer is considered adequately sensitive and specific for cancer screening. It is expected that the results of multiple markers will need to be combined in order to yield adequately accurate classification. Typically, the objective function that is optimized for combining markers is the likelihood function. In this article, we consider an alternative objective function-the area under the empirical receiver operating characteristic curve (AUC). We note that it yields consistent estimates of parameters in a generalized linear model for the risk score but does not require specifying the link function. Like logistic regression, it yields consistent estimation with case-control or cohort data. Simulation studies suggest that AUC-based classification scores have performance comparable with logistic likelihood-based scores when the logistic regression model holds. Analysis of data from a proteomics biomarker study shows that performance can be far superior to logistic regression derived scores when the logistic regression model does not hold. Model fitting by maximizing the AUC rather than the likelihood should be considered when the goal is to derive a marker combination score for classification or prediction.  相似文献   

2.
This article presents a method for estimating the accuracy of psychological screening scales using receiver operating characteristic curves and associated statistics. Screening scales are typically semicontinuous within a known range with distributions that are nearly symmetric when the target condition is present and highly skewed when the condition is absent. We model screening scale outcomes using truncated normal distributions that accommodate these different distributional shapes and use subject-specific random effects to adjust for multiple assessments within individuals. Using the proposed model, we estimate the accuracy of the Symptom Checklist as a measure of major depression from a repeatedly screened sample of patients.  相似文献   

3.
    

Background

In silico models have recently been created in order to predict which genetic variants are more likely to contribute to the risk of a complex trait given their functional characteristics. However, there has been no comprehensive review as to which type of predictive accuracy measures and data visualization techniques are most useful for assessing these models.

Methods

We assessed the performance of the models for predicting risk using various methodologies, some of which include: receiver operating characteristic (ROC) curves, histograms of classification probability, and the novel use of the quantile-quantile plot. These measures have variable interpretability depending on factors such as whether the dataset is balanced in terms of numbers of genetic variants classified as risk variants versus those that are not.

Results

We conclude that the area under the curve (AUC) is a suitable starting place, and for models with similar AUCs, violin plots are particularly useful for examining the distribution of the risk scores.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-1616-z) contains supplementary material, which is available to authorized users.  相似文献   

4.
Receiver operating characteristic (ROC) curves are used to describe the performance of diagnostic procedures. This paper proposes a simple method for the statistical comparison of two ROC curves derived from the same set of patients and the same set of healthy subjects. Generalization to studies involving more than two screening factors is straightforward. This method does not require the calculation of variances of the areas or difference of areas under the curves.  相似文献   

5.
Pepe MS  Cai T 《Biometrics》2004,60(2):528-535
The idea of using measurements such as biomarkers, clinical data, or molecular biology assays for classification and prediction is popular in modern medicine. The scientific evaluation of such measures includes assessing the accuracy with which they predict the outcome of interest. Receiver operating characteristic curves are commonly used for evaluating the accuracy of diagnostic tests. They can be applied more broadly, indeed to any problem involving classification to two states or populations (D= 0 or 1). We show that the ROC curve can be interpreted as a cumulative distribution function for the discriminatory measure Y in the affected population (D= 1) after Y has been standardized to the distribution in the reference population (D= 0). The standardized values are called placement values. If the placement values have a uniform(0, 1) distribution, then Y is not discriminatory, because its distribution in the affected population is the same as that in the reference population. The degree to which the distribution of the standardized measure differs from uniform(0, 1) is a natural way to characterize the discriminatory capacity of Y and provides a nontraditional interpretation for the ROC curve. Statistical methods for making inference about distribution functions therefore motivate new approaches to making inference about ROC curves. We demonstrate this by considering the ROC-GLM regression model and observing that it is equivalent to a regression model for the distribution of placement values. The likelihood of the placement values provides a new approach to ROC parameter estimation that appears to be more efficient than previously proposed methods. The method is applied to evaluate a pulmonary function measure in cystic fibrosis patients as a predictor of future occurrence of severe acute pulmonary infection requiring hospitalization. Finally, we note the relationship between regression models for the mean placement value and recently proposed models for the area under the ROC curve which is the classic summary index of discrimination.  相似文献   

6.
    
Rosner B  Glynn RJ 《Biometrics》2009,65(1):188-197
Summary .  The Wilcoxon Mann-Whitney (WMW) U test is commonly used in nonparametric two-group comparisons when the normality of the underlying distribution is questionable. There has been some previous work on estimating power based on this procedure ( Lehmann, 1998 , Nonparametrics ). In this article, we present an approach for estimating type II error, which is applicable to any continuous distribution, and also extend the approach to handle grouped continuous data allowing for ties. We apply these results to obtaining standard errors of the area under the receiver operating characteristic curve (AUROC) for risk-prediction rules under H 1 and for comparing AUROC between competing risk prediction rules applied to the same data set. These results are based on SAS -callable functions to evaluate the bivariate normal integral and are thus easily implemented with standard software.  相似文献   

7.
  总被引:2,自引:0,他引:2  
Dodd LE  Pepe MS 《Biometrics》2003,59(3):614-623
Accurate diagnosis of disease is a critical part of health care. New diagnostic and screening tests must be evaluated based on their abilities to discriminate diseased from nondiseased states. The partial area under the receiver operating characteristic (ROC) curve is a measure of diagnostic test accuracy. We present an interpretation of the partial area under the curve (AUC), which gives rise to a nonparametric estimator. This estimator is more robust than existing estimators, which make parametric assumptions. We show that the robustness is gained with only a moderate loss in efficiency. We describe a regression modeling framework for making inference about covariate effects on the partial AUC. Such models can refine knowledge about test accuracy. Model parameters can be estimated using binary regression methods. We use the regression framework to compare two prostate-specific antigen biomarkers and to evaluate the dependence of biomarker accuracy on the time prior to clinical diagnosis of prostate cancer.  相似文献   

8.
  总被引:1,自引:1,他引:1  
Huang Y  Sullivan Pepe M  Feng Z 《Biometrics》2007,63(4):1181-1188
Consider a continuous marker for predicting a binary outcome. For example, the serum concentration of prostate specific antigen may be used to calculate the risk of finding prostate cancer in a biopsy. In this article, we argue that the predictive capacity of a marker has to do with the population distribution of risk given the marker and suggest a graphical tool, the predictiveness curve, that displays this distribution. The display provides a common meaningful scale for comparing markers that may not be comparable on their original scales. Some existing measures of predictiveness are shown to be summary indices derived from the predictiveness curve. We develop methods for making inference about the predictiveness curve, for making pointwise comparisons between two curves, and for evaluating covariate effects. Applications to risk prediction markers in cancer and cystic fibrosis are discussed.  相似文献   

9.
    
We consider profile-likelihood inference based on the multinomial distribution for assessing the accuracy of a diagnostic test. The methods apply to ordinal rating data when accuracy is assessed using the area under the receiver operating characteristic (ROC) curve. Simulation results suggest that the derived confidence intervals have acceptable coverage probabilities, even when sample sizes are small and the diagnostic tests have high accuracies. The methods extend to stratified settings and situations in which the ratings are correlated. We illustrate the methods using data from a clinical trial on the detection of ovarian cancer.  相似文献   

10.
11.
目的

探讨儿童抽动障碍(TD)患者肠道菌群分布特点及其与病情严重程度的关系,为该类患者的治疗提供参考。

方法

选取2022年11月至2024年6月我院收治的120例TD患儿作为研究对象,收集患儿临床资料,依据耶鲁综合抽动严重程度量表(YGTSS)评分将TD患儿分为轻中度TD组(n=79)和重度TD组(n=41)。比较两组患儿临床资料,采用二元Logistic回归分析影响儿童重度TD发生的危险因素,采用受试者工作曲线(ROC)分析肠道菌群分布特点对儿童重度TD发生的预测作用。

结果

两组患儿性别、年龄、BMI、出生胎龄、出生体质量、出生疾病史、病程、常住地、家庭平均月收入、家族抽动障碍史及家族精神疾病史比较差异均无统计学意义(均P>0.05)。重度TD组患儿混合性抽动、主要照料者文化程度为高中及以下的占比高于轻中度TD组,肠道双歧杆菌、乳杆菌数量低于轻中度TD组,大肠埃希菌、乳状瘤胃球菌、粪杆菌数量高于轻中度TD组(均P<0.05)。二元Logistic回归分析显示,较高的大肠埃希菌、乳状瘤胃球菌、粪杆菌数量是影响重度TD发生的危险因素(均P<0.05)。ROC分析显示,大肠埃希菌数量预测重度TD发生的最佳截断点为6.909 lg CFU/g,AUC为0.831;乳状瘤胃球菌水平预测重度TD发生的最佳截断点为8.874 lg CFU/g,AUC为0.868;粪杆菌水平预测重度TD发生的最佳截断点为7.589 lg CFU/g,AUC为0.823;三者联合检测AUC为0.967。

结论

较高的大肠埃希菌、乳状瘤胃球菌、粪杆菌数量是影响儿童重度TD发生的危险因素。大肠埃希菌、乳状瘤胃球菌及粪杆菌水平对儿童重度TD的发生具有较高的预测评估作用,且联合预测价值更高。

  相似文献   

12.
    
Receiver operating characteristic (ROC) analysis is a useful evaluative method of diagnostic accuracy. A Bayesian hierarchical nonlinear regression model for ROC analysis was developed. A validation analysis of diagnostic accuracy was conducted using prospective multi-center clinical trial prostate cancer biopsy data collected from three participating centers. The gold standard was based on radical prostatectomy to determine local and advanced disease. To evaluate the diagnostic performance of PSA level at fixed levels of Gleason score, a normality transformation was applied to the outcome data. A hierarchical regression analysis incorporating the effects of cluster (clinical center) and cancer risk (low, intermediate, and high) was performed, and the area under the ROC curve (AUC) was estimated.  相似文献   

13.
14.
    
We compare several nonparametric and parametric weighting methods for the adjustment of the effect of strata. In particular, we focus on the adjustment methods in the context of receiver‐operating characteristic (ROC) analysis. Nonparametrically, rank‐based van Elteren's test and inverse‐variance (IV) weighting using the area under the ROC curve (AUC) are examined. Parametrically, the stratified t‐test and IV AUC weighted method are applied based on a binormal monotone transformation model. Stratum‐specific, pooled, and adjusted estimates are obtained. The pooled and adjusted AUCs are estimated. We illustrate and compare these weighting methods on a multi‐center diagnostic trial and through extensive Monte‐Carlo simulations.  相似文献   

15.
    
We consider the power and sample size calculation of diagnostic studies with normally distributed multiple correlated test results. We derive test statistics and obtain power and sample size formulas. The methods are illustrated using an example of comparison of CT and PET scanner for detecting extra-hepatic disease for colorectal cancer.  相似文献   

16.
    
Yuan Z  Ghosh D 《Biometrics》2008,64(2):431-439
Summary .   In medical research, there is great interest in developing methods for combining biomarkers. We argue that selection of markers should also be considered in the process. Traditional model/variable selection procedures ignore the underlying uncertainty after model selection. In this work, we propose a novel model-combining algorithm for classification in biomarker studies. It works by considering weighted combinations of various logistic regression models; five different weighting schemes are considered in the article. The weights and algorithm are justified using decision theory and risk-bound results. Simulation studies are performed to assess the finite-sample properties of the proposed model-combining method. It is illustrated with an application to data from an immunohistochemical study in prostate cancer.  相似文献   

17.
  总被引:1,自引:0,他引:1  
Venkatraman ES 《Biometrics》2000,56(4):1134-1138
We developed a permutation test in our earlier paper (Venkatraman and Begg, 1996, Biometrika 83, 835-848) to test the equality of receiver operating characteristic curves based on continuous paired data. Here we extend the underlying concepts to develop a permutation test for continuous unpaired data, and we study its properties through simulations.  相似文献   

18.
    
Summary In medical research, the receiver operating characteristic (ROC) curves can be used to evaluate the performance of biomarkers for diagnosing diseases or predicting the risk of developing a disease in the future. The area under the ROC curve (ROC AUC), as a summary measure of ROC curves, is widely utilized, especially when comparing multiple ROC curves. In observational studies, the estimation of the AUC is often complicated by the presence of missing biomarker values, which means that the existing estimators of the AUC are potentially biased. In this article, we develop robust statistical methods for estimating the ROC AUC and the proposed methods use information from auxiliary variables that are potentially predictive of the missingness of the biomarkers or the missing biomarker values. We are particularly interested in auxiliary variables that are predictive of the missing biomarker values. In the case of missing at random (MAR), that is, missingness of biomarker values only depends on the observed data, our estimators have the attractive feature of being consistent if one correctly specifies, conditional on auxiliary variables and disease status, either the model for the probabilities of being missing or the model for the biomarker values. In the case of missing not at random (MNAR), that is, missingness may depend on the unobserved biomarker values, we propose a sensitivity analysis to assess the impact of MNAR on the estimation of the ROC AUC. The asymptotic properties of the proposed estimators are studied and their finite‐sample behaviors are evaluated in simulation studies. The methods are further illustrated using data from a study of maternal depression during pregnancy.  相似文献   

19.
Systems biology uses systems of mathematical rules and formulas to study complex biological phenomena. In cancer research there are three distinct threads in systems biology research: modeling biology or biophysics with the goal of establishing plausibility or obtaining insights, modeling based on statistics, bioinformatics, and reverse engineering with the goal of better characterizing the system, and modeling with the goal of clinical predictions. Using illustrative examples we discuss these threads in the context of cancer research.  相似文献   

20.
Sixteen individuals with hypohidrotic ectodermal dysplasia (HED) were compared to normal standards as well as to 16 unaffected family members by using a series of 20 anthropometric measurements of the head and face. Individuals with HED were generally smaller than normal controls or their unaffected relatives. However, this size reduction was not uniform. Instead, it was most evident in the anterior-posterior dimensions of the lower two-thirds of the face, in facial height, and in the size of the ears, nose, and mouth. A stepwise discriminant function analysis indicated that a function constructed from four variables (depth of the lower face, width of the nose, mandibular arc, and total facial height) could accurately classify 96.7% of the 32 individuals in the combined sample of affected and unaffected individuals. These findings demonstrated that the face of individuals with HED is unique and can be useful in its diagnosis. Additional studies are needed to determine if similar-though-less-pronounced facial abnormalities can be used to detect minimally affected gene carriers of this presumably X-linked condition.  相似文献   

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