Statistical Methods in Diagnostic Medicine [Hardcover]
Author: Amazon Prime | Language: English | ISBN: 0470183144 | Format: PDF, EPUB
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" . . . the book is a valuable addition to the literature in the field, serving as a much-needed guide for both clinicians and advanced students."—Zentralblatt MATH
A new edition of the cutting-edge guide to diagnostic tests in medical research
In recent years, a considerable amount of research has focused on evolving methods for designing and analyzing diagnostic accuracy studies. Statistical Methods in Diagnostic Medicine, Second Edition continues to provide a comprehensive approach to the topic, guiding readers through the necessary practices for understanding these studies and generalizing the results to patient populations.
Following a basic introduction to measuring test accuracy and study design, the authors successfully define various measures of diagnostic accuracy, describe strategies for designing diagnostic accuracy studies, and present key statistical methods for estimating and comparing test accuracy. Topics new to the Second Edition include:
Methods for tests designed to detect and locate lesions
Recommendations for covariate-adjustment
Methods for estimating and comparing predictive values and sample size calculations
Correcting techniques for verification and imperfect standard biases
Sample size calculation for multiple reader studies when pilot data are available
Updated meta-analysis methods, now incorporating random effects
Three case studies thoroughly showcase some of the questions and statistical issues that arise in diagnostic medicine, with all associated data provided in detailed appendices. A related web site features Fortran, SAS®, and R software packages so that readers can conduct their own analyses.
Statistical Methods in Diagnostic Medicine, Second Edition is an excellent supplement for biostatistics courses at the graduate level. It also serves as a valuable reference for clinicians and researchers working in the fields of medicine, epidemiology, and biostatistics.
- Hardcover: 592 pages
- Publisher: Wiley; 2 edition (March 29, 2011)
- Language: English
- ISBN-10: 0470183144
- ISBN-13: 978-0470183144
- Product Dimensions: 9.4 x 6.3 x 1.2 inches
- Shipping Weight: 2 pounds (View shipping rates and policies)
- Amazon Best Sellers Rank: #991,080 in Books (See Top 100 in Books)
List of Tables xxiii
0.1 Preface xxix
0.2 Acknowledgements xxx
Part I. Basic Concepts and Methods
1. Introduction 3
1.1 Diagnostic Test Accuracy Studies 3
1.2 Case Studies 6
1.3 Software 10
1.4 Topics Not Covered in This Book 10
2. Measures of Diagnostic Accuracy 13
2.1 Sensitivity and Specificity 14
2.2 Combined Measures of Sensitivity and Specificity 21
2.3 Receiver Operating Characteristic (ROC) Curve 24
2.4 Area Under the ROC Curve 27
2.5 Sensitivity at Fixed EPR 34
2.6 Partial Area Under the ROC Curve 35
2.7 Likelihood Ratios 36
2.8 ROC Analysis When the True Diagnosis Is not Binary 41
2.9 C-Statistics and Other Measures to Compare Prediction Models 43
2.10 Detection and Localization of Multiple Lesions 44
2.11 Positive and Negative Predictive Values, Bayes Theorem, and Case Study 2 47
2.12 Optimal Decision Threshold on the ROC Curve 51
2.13 Interpreting the Results of Multiple Tests 54
3. Design of Diagnostic Accuracy Studies 57
3.1 Establish the Objective of the Study 58
3.2 Identify the Target Patient Population 63
3.3 Select a Sampling Plan for Patients 64
3.4 Select the Gold Standard 72
3.5 Choose A Measure of Accuracy 79
3.6 Identify Target Reader Population 82
3.7 Select Sampling Plan for Readers 83
3.8 Plan Data Collection 84
3.9 Plan Data Analyses 94
3.10 Determine Sample Size 101
4. Estimation and Hypothesis Testing in a Single Sample 103
4.1 Binary-Scale Data 104
4.2 Ordinal-Scale Data 117
4.3 Continuous-Scale Data 141
4.4 Testing the Hypothesis that the ROC Curve Area or Partial Area Is a Specific Value 163
5. Comparing the Accuracy of Two Diagnostic Tests 165
5.1 Binary-Scale Data 166
5.2 Ordinal- and Continuous-Scale Data 174
5.3 Tests of Equivalence 189
6. Sample Size Calculations 193
6.1 Studies Estimating the Accuracy of a Single Test 194
6.2 Sample Size for Detecting a Difference in Accuracies of Two Tests 203
6.3 Sample Size for Assessing Non-Inferiority of Equivalency of Two Tests 214
6.4 Sample Size for Determining a Suitable Cutoff Value 218
6.5 Sample Size Determination for Multi-Reader Studies 219
6.6 Alternative to Sample Size Formulae 228
7. Introduction to Meta-analysis for Diagnostic Accuracy Studies 231
7.1 Objectives 232
7.2 Retrieval of the Literature 233
7.3 Inclusion/Exclusion Criteria 237
7.4 Extracting Information from the Literature 241
7.5 Statistical Analysis 243
7.6 Public Presentation 258
Part II. Advanced Methods
8. Regression Analysis for Independent ROC Data 263
8.1 Four Clinical Studies 264
8.2 Regression Models for Continuous-Scale Tests 267
8.3 Regression Models for Ordinal-Scale Tests 287
8.4 Covariate Adjusted ROC Curves of Continuous-Scale tests 294
9. Analysis of Multiple Reader and/or Multiple Test Studies 297
9.1 Studies Comparing Multiple Tests with Covariates 298
9.2 Studies with Multiple Readers and Multiple Tests 310
9.3 Analysis of Multiple Tests Designed to Locate and Diagnose Lesions 325
10. Methods for Correcting Verification Bias 329
10.1 Examples 330
10.2 Impact of Verification Bias 333
10.3 A Single Binary-Scale Test 334
10.4 Correlated Binary-Scale Tests 341
10.5 A Single Ordinal-Scale Test 348
10.6 Correlated Ordinal-Scale Tests 360
10.7 Continuous-Scale Tests 372
11. Methods for Correcting Imperfect Gold Standard Bias 389
11.1 Examples 390
11.2 Impact of Imperfect Gold Standard Bias 393
11.3 One Single Binary test in a Single Population 395
11.4 One Single Binary test in G Populations 402
11.5 Multiple Binary Tests in One Single Population 408
11.6 Multiple Binary Tests in G Populations 423
11.7 Multiple Ordinal-Scale Tests in One Single Population 425
11.8 Multiple-Scale Tests in One Single Population 429
12. Statistical Analysis for Meta-analysis 435
12.1 Binary-Scale Data 436
12.2 Ordinal- or Continuous-Scale Data 438
12.3 ROC Curve Area 445
Appendix A. Case Studies and Chapter 8 Data 449
Appendix B. Jackknife and Bootstrap Methods of Estimating Variances and Confidence Intervals 477
There are two modern books in the field. This one by Profs Zhou, Obuchowski, McClish and the book by Prof. Pepe. All four are experts in this field. Both books present the same aspects of statistical diagnostic testing and both can be of invaluable help for researchers (both applied and more academic) and graduate students. However, Professor Pepe has done an excellent job (if I may) using a clear, concise notation and language throughout. On the other hand this book (ZOM) is not that well written, giving more weight in the presentation of the personal research of the authors. As a result there is some notation inconsistency (not too puzzling though) and the flow of the text is not that smooth. Both books have full reference lists, they present interesting applications and give a number of exercises at the end of each chapter.By StatAge
Another reviewer has compared this book with the one by Pepe but is not aware of the book by Broemeling. I reviewed Broemelings book and have this one so in addition to discussing the features of the Zhou-Obuchowski-McClish book I will make some comments comparing it to Lyle Broemeling's book.By Michael R. Chernick
Although this book was published in 2002 it is still very contemporary and useful. Both the classical and Bayesian approaches are covered but the details of Bayesian approaches using MCMC methods is not here so if you are interested in that it is well-covered in Broemeling's book. This book is comprehensive and rigorous and show all the modern techniques including the bootstrap. A published article on a bootstrap approach to a diagnostic testing problem involving a mixed linear model is covered in detail and critiqued for depending on an independence assumption.
What I like most about the are the last two chapters 11 and 12. This is material I have not seen before with chapter 11 showing the types of bias that can occur when the gold standard is imperfect (a very common problem given very thorough answers here). Chapter 12 provides statistical methods for habdling multiple studies for evaluating 1) sensitivity and specificity for a diagnostic test and 2) ROC area estimates ofor a diagnostic test using fixed and random effects models.
Also chapter 6 provides methods for estimating sample size when determining area under ROC curves and sensitivity and specificity for single tests, comparisons of two tests, determining equivalence of two tests and more. Methods are illustrated using real examples.
At a time when biomarkers are starting to be used as diagnostics this methodology becomes extremely important.
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