Diagnostic and Biomarkers Statistical (DABS) Center -- Book

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.

 
Table of Contents
 
  1. Introduction
  2. Measures of Accuracy for Binary Tests
  3. Comparing Binary Tests and Regression Analysis
  4. The Receiver Operating Characteristic Curve
  5. Estimating the ROC Curve
  6. Covariate Effects on Continuous and Ordinal Tests
  7. Incomplete Data and Imperfect Reference Tests
  8. Study Design and Hypothesis Testing
  9. More Topics and Conclusions
    References/Bibliography
    Index
  • Introduction
  • Measures of Accuracy for Binary Tests
  • Comparing Binary Tests and Regression Analysis
  • The Receiver Operating Characteristic Curve
  • Estimating the ROC Curve
  • Covariate Effects on Continuous and Ordinal Tests
  • Incomplete Data and Imperfect Reference Tests
  • Study Design and Hypothesis Testing
  • More Topics and Conclusions
    References/Bibliography
    Index
  • Introduction
  • Measures of Accuracy for Binary Tests
  • Comparing Binary Tests and Regression Analysis
  • The Receiver Operating Characteristic Curve
  • Estimating the ROC Curve
  • Covariate Effects on Continuous and Ordinal Tests
  • Incomplete Data and Imperfect Reference Tests
  • Study Design and Hypothesis Testing
  • More Topics and Conclusions
    References/Bibliography
    Index
  • 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.

    Table of Contents

     

    Datasets

    Datasets used for the examples in the book are available for download from the DABS datasets page.

     

    Programs

    Downloadable Stata programs and help files

    Stata version 7 or higher required for most programs; version 8 or 9 required for some as updates and additions become available.

    • emroc.ado, emroc.hlp - Plot the empirical ROC curve and optionally return plot coordinates. Calculate a nonparametric estimate of the area under the ROC curve (AUC) or partial AUC.
    • dfroc.ado, dfroc.hlp - Calculate the distribution-free estimator of the ROC curve within a GLM binary regression framework. Obtain bootstrap standard error estimates for the binormal ROC parameters and correponding AUC.
    • aucbs.ado, aucbs.hlp - Calculate a nonparametric estimate of the area under the ROC curve (AUC) and bootstrapped standard error estimates. Optionally calculate the partial AUC or empirical ROC(t) for specified t and corresponding se estimates. With data for two test measures, difference statistics for the AUC, pAUC, and ROC(t)) are calculated.
    • rocsize.ado, rocsize.hlp - Determine power for a one-sample screening study; continuous data.
    • aucsize.ado, aucsize.hlp - Determine power for a one-sample screening study based on ROC area under the curve (AUC) improvement
    • scrsize.ado, scrsize.hlp - Determine power for a one-sample screening study; binary test outcome data.
    • binscrn1.ado, binscrn1.hlp - Calculates summary screening measures for a test with binary outcome.
    • binscrn2.ado, binscrn2.hlp - Comparison of 2 binary screening tests; for unpaired data.
    • binscrn3.ado, binscrn3.hlp - Comparison of 2 binary screening tests; for paired data.
    • lrreg.ado, lrreg_ll.ado, lrreg.hlp - Diagnostic Likelihood Ratio (DLR) regression.

    Utility programs used by text example do-files

    • binormroc.ado, binormroc.hlp - Plots the binormal ROC for specified normal case and control distributions of a test measure.
    • bvnellip.ado, bvnellip.hlp - Calculates a confidence ellipse for the joint distribution of 2 parameters. The parameters are assumed to have a bivariate normal distribution.
    • semt_profile.ado - Specifies data directory path and log file target path for text example do-files.

     

    Examples

    Stata do-files for selected text examples and corresponding figures

    Chapter 2

    example 2.3 (figure 2.2)

    Chapter 3

    example 3.1 (figure 3.1)

    example 3.2

    example 3.4

    example 3.5

    example 3.6

    example 3.7

    example 3.8

    Chapter 4

    example 4.1 (figure 4.4)

    Chapter 5

    example 5.1 (figure 5.1, figure 5.2)

    example 5.2 (figure 5.4)

    example 5.4 (figure 5.5, figure 5.5b)

    example 5.5 (figure 5.6)

    example 5.6 (figure 5.8, figure 5.8b)

    example 5.7 (figure 5.9)

    Chapter 6

    example 6.1 (figure 6.1)

    example 6.6 (incl figure 6.5)

    example 6.7 (incl figure 6.6)

    example 6.11

    example 6.12 (figure 6.8)

    example 6.13 (figure 6.9)

    Chapter 7

    example 7.3

    example 7.4

    example 7.5

    Chapter 9

    example 9.1 (incl figure 9.1 )