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Statistical Evaluation of Medical Tests for Classification and Prediction

ISBN-10: 0198565828
ISBN-13: 9780198565826
Edition: 2004
List price: $110.00
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Description: This book describes statistical concepts and techniques for evaluating medical diagnostic tests and biomarkers for detecting disease. More generally, the techniques pertain to the statistical classification problem for predicting a dichotomous  More...

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Book details

List price: $110.00
Copyright year: 2004
Publisher: Oxford University Press, Incorporated
Publication date: 12/16/2004
Binding: Paperback
Pages: 320
Size: 6.25" wide x 9.25" long x 0.75" tall
Weight: 1.276
Language: English

This book describes statistical concepts and techniques for evaluating medical diagnostic tests and biomarkers for detecting disease. More generally, the techniques pertain to the statistical classification problem for predicting a dichotomous outcome. Measures for quantifying test accuracy are described including sensitivity, specificity, predictive values, diagnostic likelihood ratios and the Receiver Operating Characteristic Curve that is commonly used for continuous and ordinal valued tests. Statistical procedures are presented for estimating and comparing them. Regression frameworks for assessing factors that influence test accuracy and for comparing tests while adjusting for such factors are presented. This book presents many worked examples of real data and should be of interest to practicing statisticians or quantitative researchers involved in the development of tests for classification or prediction in medicine.

Notation
Introduction
The medical test
Tests, classification and the broader context
Disease screening versus diagnosis
Criteria for a useful medical test
Elements of study design
Scale for the test result
Selection of study subjects
Comparing tests
Test integrity
Sources of bias
Examples and datasets
Overview
The CASS dataset
Pancreatic cancer serum biomarkers study
Hepatitis metastasis ultrasound study
CARET PSA biomarker study
Ovarian cancer gene expression study
Neonatal audiology data
St Louis prostate cancer screening study
Topics and organization
Exercises
Measures of accuracy for binary tests
Measures of accuracy
Notation
Disease-specific classification probabilities
Predictive values
Diagnostic likelihood ratios
Estimating accuracy with data
Data from a cohort study
Proportions: (FPF, TPF) and (PPV, NPV)
Ratios of proportions: DLRs
Estimation from a case-control study
Merits of case-control versus cohort studies
Quantifying the relative accuracy of tests
Comparing classification probabilities
Comparing predictive values
Comparing diagnostic likelihood ratios
Which test is better?
Concluding remarks
Exercises
Comparing binary tests and regression analysis
Study designs for comparing tests
Unpaired designs
Paired designs
Comparing accuracy with unpaired data
Empirical estimators of comparative measures
Large sample inference
Comparing accuracy with paired data
Sources of correlation
Estimation of comparative measures
Wide or long data representations
Large sample inference
Efficiency of paired versus unpaired designs
Small sample properties
The CASS study
The regression modeling framework
Factors potentially affecting test performance
Questions addressed by regression modeling
Notation and general set-up
Regression for true and false positive fractions
Binary marginal GLM models
Fitting marginal models to data
Illustration: factors affecting test accuracy
Comparing tests with regression analysis
Regression modeling of predictive values
Model formulation and fitting
Comparing tests
The incremental value of a test for prediction
Regression models for DLRs
The model form
Fitting the DLR model
Comparing DLRs of two tests
Relationships with other regression models
Concluding remarks
Exercises
The receiver operating characteristic curve
The context
Examples of non-binary tests
Dichotomizing the test result
The ROC curve for continuous tests
Definition of the ROC
Mathematical properties of the ROC curve
Attributes of and uses for the ROC curve
Restrictions and alternatives to the ROC curve
Summary indices
The area under the ROC curve (AUC)
The ROC(t[subscript 0]) and partial AUC
Other summary indices
Measures of distance between distributions
The binormal ROC curve
Functional form
The binormal AUC
The binormal assumption
The ROC for ordinal tests
Tests with ordered discrete results
The latent decision variable model
Identification of the latent variable ROC
Changes in accuracy versus thresholds
The discrete ROC curve
Summary measures for the discrete ROC curve
Concluding remarks
Exercises
Estimating the ROC curve
Introduction
Approaches
Notation and assumptions
Empirical estimation
The empirical estimator
Sampling variability at a threshold
Sampling variability of ROC[subscript e](t)
The empirical AUC and other indices
Variability in the empirical AUC
Comparing empirical ROC curves
Illustration: pancreatic cancer biomarkers
Discrete ordinal data ROC curves
Modeling the test result distributions
Fully parametric modeling
Semiparametric location-scale models
Arguments against modeling test results
Parametric distribution-free methods: ordinal tests
The binormal latent variable framework
Fitting the discrete binormal ROC function
Generalizations and comparisons
Parametric distribution-free methods: continuous tests
LABROC
The ROC-GLM estimator
Inference with parametric distribution-free methods
Concluding remarks
Exercises
Proofs of theoretical results
Covariate effects on continuous and ordinal tests
How and why?
Notation
Aspects to model
Omitting covariates/pooling data
Reference distributions
Non-diseased as the reference population
The homogenous population
Nonparametric regression quantiles
Parametric estimation of S[subscript D,Z]
Semiparametric models
Application
Ordinal test results
Modeling covariate effects on test results
The basic idea
Induced ROC curves for continuous tests
Semiparametric location-scale families
Induced ROC curves for ordinal tests
Random effect models for test results
Modeling covariate effects on ROC curves
The ROC-GLM regression model
Fitting the model to data
Comparing ROC curves
Three examples
Approaches to ROC regression
Modeling ROC summary indices
A qualitative comparison
Concluding remarks
Exercises
Incomplete data and imperfect reference tests
Verification biased sampling
Context and definition
The missing at random assumption
Correcting for bias with Bayes' theorem
Inverse probability weighting/imputation
Sampling variability of corrected estimates
Adjustments for other biasing factors
A broader context
Non-binary tests
Verification restricted to screen positives
Extreme verification bias
Identificable parameters for a single test
Comparing tests
Evaluating covariate effects on (DP, FP)
Evaluating covariate effects on (TPF, FPF) and on prevalence
Evaluating covariate effects on (rTPF, rFPF)
Alternative strategies
Imperfect reference tests
Examples
Effects on accuracy parameters
Classic latent class analysis
Relaxing the conditional independence assumption
A critique of latent class analysis
Discrepant resolution
Composite reference standards
Concluding remarks
Exercises
Proofs of theoretical results
Study design and hypothesis testing
The phases of medical test development
Research as a process
Five phases for the development of a medical test
Sample sizes for phase 2 studies
Retrospective validation of a binary test
Retrospective validation of a continuous test
Sample size based on the AUC
Ordinal tests
Sample sizes for phase 3 studies
Comparing two binary tests--paired data
Comparing two binary tests--unpaired data
Evaluating population effects on test performance
Comparisons with continuous test results
Estimating the threshold for screen positivity
Remarks on phase 3 analyses
Sample sizes for phase 4 studies
Designs for inference about (FPF, TPF)
Designs for predictive values
Designs for (FP, DP)
Selected verification of screen negatives
Phase 5
Matching and stratification
Stratification
Matching
Concluding remarks
Exercises
More topics and conclusions
Meta-analysis
Goals of meta-analysis
Design of a meta-analysis study
The summary ROC curve
Binomial regression models
Incorporating the time dimension
The context
Incident cases and long-term controls
Interval cases and controls
Predictive values
Longitudinal measurements
Combining multiple test results
Boolean combinations
The likelihood ratio principle
Optimality of the risk score
Estimating the risk score
Development and assessment of the combination score
Concluding remarks
Topics we only mention
New applications and new technologies
Exercises
Bibliography
Index

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