Statistical Methods in Diagnostic Medicine
Statistical Methods in Diagnostic Medicine
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DOI：
10.1002/9780470906514
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发表时间：
2002-07
期刊：
影响因子：
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通讯作者：
Xiao-Hua Zhou;N. Obuchowski;D. McClish
中科院分区：
文献类型：
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作者：
Xiao-Hua Zhou;N. Obuchowski;D. McClish
前言。鸣谢。1。1.1为什么要写这本书？1.2什么是诊断准确性？1.3诊断医学统计方法的里程碑。1.4软件。1.5本书未涉及的主题。1.6摘要。一、基本概念和方法。2. 诊断准确度的测量。2.1敏感性和特异性。2.2敏感性和特异性的综合测量。2.3 ROC曲线。2.4 ROC曲线下面积。2.5在固定FPR下的敏感性。2.6 ROC曲线下的部分面积，2.7似然比，2.8其他ROC曲线指标，2.9多种异常的定位与检测，2.10诊断试验的解释，2.11 ROC曲线的最优决策阈值，2.12多项试验。诊断准确性研究的设计。3.1确定研究目的。3.2确定目标患者群体。3.3选择患者抽样计划。3.3.1第一阶段：探索性研究。3.3.2第二阶段：挑战性研究。3.3.3第三阶段：临床研究。3.4选择金标准。3.5选择准确度度量。3.6确定目标读者群体。3.7为读者选择抽样计划。3.8规划数据收集。3.8.1检验结果格式。3.8.2读者研究的数据收集。3.8.3读者培训。3.9规划数据分析。3.9.1统计假设。3.9.2报告检验结果。3.10确定样本量。4。单样本的估计和假设检验。4.1二值尺度数据。4.1.1敏感性和特异性。4.1.2聚类二值数据的敏感性和特异性。4.1.3似然比（LR）。4.1.4优势比。4.2序数尺度数据。4.2.1经验ROC曲线。4.2.2光滑曲线拟合（参数模型）。4.2.3在特定FPR处的灵敏度估计。4.2.4 ROC曲线下的面积和局部面积（参数模型）。4.2.5曲线下面积（非参数法）。4.2.6聚类数据的非参数分析。4.2.7退化数据。4.2.8参数和非参数方法的选择。4.3连续尺度数据。4.3.1经验ROC曲线。4.3.2光滑ROC曲线的拟合（参数和非参数方法）。4.3.3 ROC曲线下面积（参数和非参数）。4.3.4固定FPR的灵敏度和决策阈值，4.3.5选择最优工作点，4.3.6参数技术和非参数技术的选择，4.4关于ROC面积的假设检验。比较两种诊断试验的准确性。5.1二值尺度数据。5.1.1灵敏度和特异性。5.1.2聚类二值数据的灵敏度和特异性。5.2有序和连续尺度数据。5.2.1确定两条ROC曲线的相等性。5.2.2比较某一点的ROC曲线。5.2.3确定TPR不同的FPR范围。5.2.4区域或部分区域的比较。5.3等效性的检验。计算样本量。6.1单个试验准确度研究的样本量。6.1.1灵敏度和特异度。6.1.2 ROC曲线下面积。6.1.3固定FPR下的灵敏度。6.1.4 ROC曲线下的部分面积。6.2两项试验准确度的样本量。6.2.1灵敏度和特异度。6.2.2 ROC曲线下的面积。6.2.3固定FPR下的灵敏度。6.2.4 ROC曲线下的部分面积。6.3两个试验等效研究的样本量。6.4确定合适的截止值的样本量。诊断试验Meta分析中的问题。7.1目的。7.2文献检索。7.3纳入排除标准。7.4从文献中提取信息。7.5统计分析。7.6公开发表。先进的方法。8. 独立ROC数据的回归分析。8.1 4项临床研究。8.1.1颈动脉手术病变例。8.1.2胰腺癌例。8.1.3成人肥胖例。8.1.4前列腺癌分期例。8.2连续规模试验的回归模型。8.2.1平滑ROC曲线的间接回归模型。8.2.2平滑ROC曲线的直接回归模型。8.2.3 MRA用于颈动脉手术病变检测。8.2.4检测胰腺癌。8.2.5儿童体重指数预测成人肥胖。8.3有序量表检验的回归模型。8.3.1潜在平滑ROC曲线的间接回归模型。8.3.2潜在平滑ROC曲线的直接回归模型。8.3.3 US检测前列腺周围浸润。9. 相关ROC数据分析。9.1对同一患者进行多项检验测量的研究。9.1.1顺序量表测试的间接回归模型。9.1.2新生儿检查示例。9.1.3连续量表测试的直接回归模型。9.2多读者和多测试的研究。9.2.1诊断准确性汇总度量的混合效应方差分析模型。9.2.2 TAD示例的检测。9.2.3 Jackknife伪值的混合效应方差分析模型。9.2.4新生儿检查例：9.2.5 Bootstrap方法。9.3多读卡器研究的样本量计算。纠正验证偏倚的方法。10.1单个二值量表检验。10.1.1带有MAR假设的校正方法。10.1.2没有MAR假设的校正方法。10.1.3肝闪烁图示例。10.2相关二值量表检验。10.2.1无协变量的ML方法。10.2.2有协变量的ML方法。10.2.3痴呆障碍筛查试验示例。10.3单个顺序量表检验。10.3.1无协变量的ML方法。10.3.2不确定来源的发热例10.3.3带有协变量的ML方法，10.3.4痴呆障碍的筛选试验，10.4相关有序量表试验，10.4.1潜在平滑ROC曲线的加权GEE方法，10.4.2 ROC区域的基于似然的方法，10.4.3使用CT和MRI进行胰腺癌分期。纠正不完全标准偏差的方法。11.1单个人群中的单个检验。11.1.1假设和类圆线虫感染的例子。11.2 G人群中的单个检验。11.2.1结核病的例子。11.3单个人群中的多个检验。11.3.1 CIA下的MLEs。11.3.2胸膜增厚的评估例。11.3.3无CIA的ML入路。11.3.4艾滋病毒的生物测定例。11.4 G人群的多重二元检测。11.4.1 CIA下的ML方法。11.4.2没有CIA的ML方法。12. Meta分析的统计方法。12.1敏感性和特异性对。12.1.1一条常见SROC曲线。12.1.2研究特异性SROC曲线。12.1.3有和没有颜色指导的双超声检查的评价。12.2 ROC曲线面积。12.2.1固定效应模型。12.2.2随机效应模型。12.2.3地塞米松抑制试验的评价。索引。
Preface. Acknowledgments. 1. Introduction. 1.1 Why This Book? 1.2 What Is Diagnostic Accuracy? 1.3 Landmarks in Statistical Methods for Diagnostic Medicine. 1.4 Software. 1.5 Topics not Covered in This Book. 1.6 Summary. I BASIC CONCEPTS AND METHODS. 2. Measures of Diagnostic Accuracy. 2.1 Sensitivity and Specificity. 2.2 The Combined Measures of Sensitivity and Specificity. 2.3 The ROC Curve. 2.4 The Area Under the ROC Curve. 2.5 The Sensitivity at a Fixed FPR. 2.6 The Partial Area Under the ROC Curve. 2.7 Likelihood Ratios. 2.8 Other ROC Curve Indices. 2.9 The Localization and Detection of Multiple Abnormalities. 2.10 Interpretation of Diagnostic Tests. 2.11 Optimal Decision Threshold on the ROC Curve. 2.12 Multiple Tests. 3. The Design of Diagnostic Accuracy Studies. 3.1 Determining the Objective of the Study. 3.2 Identifying the Target Patient Population. 3.3 Selecting a Sampling Plan for Patients. 3.3.1 Phase I: Exploratory Studies. 3.3.2 Phase II: Challenge Studies. 3.3.3 Phase III: Clinical Studies. 3.4 Selecting the Gold Standard. 3.5 Choosing a Measure of Accuracy. 3.6 Identifying the Target Reader Population. 3.7 Selecting a Sampling Plan for Readers. 3.8 Planning the Data Collection. 3.8.1 Format for the Test Results. 3.8.2 Data Collection for the Reader Studies. 3.8.3 Reader Training. 3.9 Planning the Data Analyses. 3.9.1 Statistical Hypotheses. 3.9.2 Reporting the Test Results. 3.10 Determining the Sample Size. 4. Estimation and Hypothesis Testing in a Single Sample. 4.1 Binary Scale Data. 4.1.1 Sensitivity and Specificity. 4.1.2 The Sensitivity and Specificity of Clustered Binary Data. 4.1.3 The Likelihood Ratio (LR). 4.1.4 The Odds Ratio. 4.2 Ordinal Scale Data. 4.2.1 The Empirical ROC Curve. 4.2.2 Fitting a Smooth Curve (Parametric Model). 4.2.3 Estimation of Sensitivity at a Particular FPR. 4.2.4 The Area and Partial Area Under the ROC Curve (Parametric Model). 4.2.5 The Area Under the Curve (Nonparametric Method). 4.2.6 Nonparametric Analysis of Clustered Data. 4.2.7 The Degenerate Data. 4.2.8 Choosing Between Parametric and Nonparametric Methods. 4.3 Continuous Scale Data. 4.3.1 The Empirical ROC Curve. 4.3.2 Fitting a Smooth ROC Curve (Parametric and Nonparametric Methods). 4.3.3 Area Under the ROC Curve (Parametric and Nonparametric). 4.3.4 Fixed FPR The Sensitivity and Decision Threshold. 4.3.5 Choosing the Optimal Operating Point. 4.3.6 Choosing Between Parametric and Nonparametric Techniques. 4.4 Hypothesis Testing About the ROC Area. 5. Comparing the Accuracy of Two Diagnostic Tests. 5.1 Binary Scale Data. 5.1.1 Sensitivity and Specificity. 5.1.2 Sensitivity and Specificity of Clustered Binary Data. 5.2 Ordinal and Continuous Scale Data. 5.2.1 Determining the Equality of Two ROC Curves. 5.2.2 Comparing ROC Curves at a Particular Point. 5.2.3 Determining the Range of FPR for Which TPR Differ. 5.2.4 A Comparison of the Area or Partial Area. 5.3 Tests of Equivalence. 6. Sample Size Calculation. 6.1 The Sample Size for Accuracy Studies of a Single Test. 6.1.1 Sensitivity and Specificity. 6.1.2 The Area Under the ROC Curve. 6.1.3 The Sensitivity at a Fixed FPR. 6.1.4 The Partial Area Under the ROC Curve. 6.2 The Sample Size for the Accuracy of Two Tests. 6.2.1 Sensitivity and Specificity. 6.2.2 The Area Under the ROC Curve. 6.2.3 The Sensitivity at a Fixed FPR. 6.2.4 The Partial Area Under the ROC Curve. 6.3 The Sample Size for Equivalent Studies of Two Tests. 6.4 The Sample Size for Determining a Suitable Cutoff Value. 7. Issues in Meta Analysis for Diagnostic Tests. 7.1 Objectives. 7.2 Retrieval of the Literature. 7.3 Inclusion Exclusion Criteria. 7.4 Extracting Information From the Literature. 7.5 Statistical Analysis. 7.6 Public Presentation. II ADVANCED METHODS. 8. Regression Analysis for Independent ROC Data. 8.1 Four Clinical Studies. 8.1.1 Surgical Lesion in a Carotid Vessel Example. 8.1.2 Pancreatic Cancer Exampl. 8.1.3 Adult Obesity Example. 8.1.4 Staging of Prostate Cancer Example. 8.2 Regression Models for Continuous Scale Tests. 8.2.1 Indirect Regression Models for Smooth ROC Curves. 8.2.2 Direct Regression Models for Smooth ROC Curves. 8.2.3 MRA Use for Surgical Lesion Detection in the Carotid Vessel. 8.2.4 Biomarkers for the Detection of Pancreatic Cancer. 8.2.5 Prediction of Adult Obesity by Using Childhood BMI Measurements. 8.3 Regression Models for Ordinal Scale Tests. 8.3.1 Indirect Regression Models for Latent Smooth ROC Curves. 8.3.2 Direct Regression Model for Latent Smooth ROC Curves. 8.3.3 Detection of Periprostatic Invasion With US. 9. Analysis of Correlated ROC Data. 9.1 Studies With Multiple Test Measurements of the Same Patient. 9.1.1 Indirect Regression Models for Ordinal Scale Tests. 9.1.2 Neonatal Examination Example. 9.1.3 Direct Regression Models for Continuous Scale Tests. 9.2 Studies With Multiple Readers and Tests. 9.2.1 A Mixed Effects ANOVA Model for Summary Measures of Diagnostic Accuracy. 9.2.2 Detection of TAD Example. 9.2.3 The Mixed Effects ANOVA Model for Jackknife Pseudovalues. 9.2.4 Neonatal Examination Example. 9.2.5 A Bootstrap Method. 9.3 Sample Size Calculation for Multireader Studies. 10. Methods for Correcting Verification Bias. 10.1 A Single Binary Scale Test. 10.1.1 Correction Methods With the MAR Assumption. 10.1.2 Correction Methods Without the MAR Assumption. 10.1.3 Hepatic Scintigraph Example. 10.2 Correlated Binary Scale Tests. 10.2.1 An ML Approach Without Covariates. 10.2.2 An ML Approach With Covariates. 10.2.3 Screening Tests for Dementia Disorder Example. 10.3 A Single Ordinal Scale Test. 10.3.1 An ML Approach Without Covariates. 10.3.2 Fever of Uncertain Origin Example. 10.3.3 An ML Approach With Covariates. 10.3.4 Screening Test for Dementia Disorder Example. 10.4 Correlated Ordinal Scale Tests. 10.4.1 The Weighted GEE Approach for Latent Smooth ROC Curves. 10.4.2 A Likelihood Based Approach for ROC Areas. 10.4.3 Use of CT and MRI for Staging Pancreatic Cancer Example. 11. Methods for Correcting Imperfect Standard Bias. 11.1 One Single Test in a Single Population. 11.1.1 Hypothetical and Strongyloides Infection Examples. 11.2 One Single Test in G Populations. 11.2.1 Tuberculosis Example. 11.3 Multiple Tests in One Single Population. 11.3.1 MLEs Under the CIA. 11.3.2 Assessment of Pleural Thickening Example. 11.3.3 ML Approaches Without the CIA. 11.3.4 Bioassays for HIV Example. 11.4 Multiple Binary Tests in G Populations. 11.4.1 ML Approaches Under the CIA. 11.4.2 ML Approaches Without the CIA. 12. Statistical Methods for Meta Analysis. 12.1 Sensitivity and Specificity Pairs. 12.1.1 One Common SROC Curve. 12.1.2 Study Specific SROC Curve. 12.1.3 Evaluation of Duplex Ultrasonography, With and Without Color Guidance. 12.2 ROC Curve Areas. 12.2.1 Fixed Effects Models. 12.2.2 Random Effects Models. 12.2.3 Evaluation of the Dexamethasone Suppression.Test. Index.