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The statistical evaluation of medical tests for classification and prediction /
The use of clinical and laboratory information to detect conditions and predict patient outcomes is a mainstay of medical practice. Modern biotechnology offers increasing potential to develop sophisticated tests for these purposes. This book describes the statistical concepts and techniques for eval...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Oxford :
Oxford University Press,
2004.
|
| Colección: | Oxford statistical science series ;
31. |
| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- Cover; CONTENTS; Notation; 1 Introduction; 1.1 The medical test; 1.1.1 Tests, classification and the broader context; 1.1.2 Disease screening versus diagnosis; 1.1.3 Criteria for a useful medical test; 1.2 Elements of study design; 1.2.1 Scale for the test result; 1.2.2 Selection of study subjects; 1.2.3 Comparing tests; 1.2.4 Test integrity; 1.2.5 Sources of bias; 1.3 Examples and datasets; 1.3.1 Overview; 1.3.2 The CASS dataset; 1.3.3 Pancreatic cancer serum biomarkers study; 1.3.4 Hepatitis metastasis ultrasound study; 1.3.5 CARET PSA biomarker study.
- 1.3.6 Ovarian cancer gene expression study1.3.7 Neonatal audiology data; 1.3.8 St Louis prostate cancer screening study; 1.4 Topics and organization; 1.5 Exercises; 2 Measures of accuracy for binary tests; 2.1 Measures of accuracy; 2.1.1 Notation; 2.1.2 Disease-specific classification probabilities; 2.1.3 Predictive values; 2.1.4 Diagnostic likelihood ratios; 2.2 Estimating accuracy with data; 2.2.1 Data from a cohort study; 2.2.2 Proportions: (FPF, TPF) and (PPV, NPV); 2.2.3 Ratios of proportions: DLRs; 2.2.4 Estimation from a case-control study.
- 2.2.5 Merits of case-control versus cohort studies2.3 Quantifying the relative accuracy of tests; 2.3.1 Comparing classification probabilities; 2.3.2 Comparing predictive values; 2.3.3 Comparing diagnostic likelihood ratios; 2.3.4 Which test is better?; 2.4 Concluding remarks; 2.5 Exercises; 3 Comparing binary tests and regression analysis; 3.1 Study designs for comparing tests; 3.1.1 Unpaired designs; 3.1.2 Paired designs; 3.2 Comparing accuracy with unpaired data; 3.2.1 Empirical estimators of comparative measures; 3.2.2 Large sample inference; 3.3 Comparing accuracy with paired data.
- 3.3.1 Sources of correlation3.3.2 Estimation of comparative measures; 3.3.3 Wide or long data representations; 3.3.4 Large sample inference; 3.3.5 Efficiency of paired versus unpaired designs; 3.3.6 Small sample properties; 3.3.7 The CASS study; 3.4 The regression modeling framework; 3.4.1 Factors potentially affecting test performance; 3.4.2 Questions addressed by regression modeling; 3.4.3 Notation and general set-up; 3.5 Regression for true and false positive fractions; 3.5.1 Binary marginal GLM models; 3.5.2 Fitting marginal models to data.
- 3.5.3 Illustration: factors affecting test accuracy3.5.4 Comparing tests with regression analysis; 3.6 Regression modeling of predictive values; 3.6.1 Model formulation and fitting; 3.6.2 Comparing tests; 3.6.3 The incremental value of a test for prediction; 3.7 Regression models for DLRs; 3.7.1 The model form; 3.7.2 Fitting the DLR model; 3.7.3 Comparing DLRs of two tests; 3.7.4 Relationships with other regression models; 3.8 Concluding remarks; 3.9 Exercises; 4 The receiver operating characteristic curve; 4.1 The context; 4.1.1 Examples of non-binary tests.