The statistical evaluation of medical tests for classification and prediction / Margaret Sullivan Pepe.

Format:

Book

Author/Creator:

Pepe, Margaret Sullivan, 1961- author.

Series:

Oxford statistical science series ; 28.

Oxford scholarship online.

Oxford scholarship online

Language:

English

Subjects (All):

Medical statistics.

Diagnosis, Laboratory--Research--Statistical methods.

Diagnosis, Laboratory.

Physical Description:

1 online resource (319 p.)

Place of Publication:

Oxford : Oxford University Press, 2023.

Language Note:

English

Summary:

Margaret Sullivan Pepe describes statistical concepts and techniques for evaluating medical diagnostic tests and biomarkers, which are chemicals added to the body for measuring the progress of disease or the effects of treatment. The statistical results can be used for detecting disease.

Contents:

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

4.1.2 Dichotomizing the test result

Notes:

Previously issued in print: 2003.

Includes bibliographical references and index.

Derived record based on print version record and publisher information.

ISBN:

1-383-02220-8

0-19-856582-8

0-19-158861-X

OCLC:

1406781232