Comparing Clinical Measurement Methods: A practical guide / Edition 1

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Overview

This book provides a practical guide to analysis of simple and complex method comparison data, using Stata, SAS and R. It takes the classical Limits of Agreement as a starting point, and presents it in a proper statistical framework. The model serves as a reference for reporting sources of variation and for providing conversion equations and plots between methods for practical use, including prediction uncertainty.

  • Presents a modeling framework for analysis of data and reporting of results from comparing measurements from different clinical centers and/or different methods.
  • Provides the practical tools for analyzing method comparison studies along with guidance on what to report and how to plan comparison studies and advice on appropriate software.
  • Illustrated throughout with computer examples in R.
  • Supported by a supplementary website hosting an R-package that performs the major part of the analyses needed in the area.
  • Examples in SAS and Stata for the most common situations are also provided.
  • Written by an acknowledged expert on the subject, with a long standing experience as a biostatistician in a clinical environment and a track record of delivering training on the subject.

Biostatisticians, clinicians, medical researchers and practitioners involved in research and analysis of measurement methods and laboratory investigations will benefit from this book. Students of statistics, biostatistics, and the chemical sciences will also find this book useful.

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Editorial Reviews

Doody's Review Service
Reviewer: Mary E Charlton, PhD (University of Iowa College of Public Health)
Description: This book describes a modeling framework for analysis and interpretation of data comparing measurement methods, and includes practical examples of the application of methods in some specific situations using Stata and SAS.
Purpose: The purpose is to provide practical tools to help readers interpret and conduct method comparison studies. Given the challenges of conducting these types of studies that involve the need for both clinical and statistical perspectives, this is a worthy objective, which the book meets.
Audience: The author assumes that his readers are familiar with standard statistical theory, linear models, and mixed models, but he also states that the general concepts can be understood by a more general audience. I think those without a statistical background would likely struggle to understand even the general ideas.
Features: Most of the early chapters in the book describe various common situations, such as two methods with a single measurement on each, replicate measurements, and several methods of measurements with single and replicate measures. Useful summaries of how to proceed with analyses appear at the end of most chapters. Chapter 7 describes a modeling framework to compare measurements taken from different methods. The later chapters describe transformation of data, and include discussions on repeatability, reproducibility, measures of association and agreement, and design of method comparison studies. The final chapters contain examples using SAS and Stata and information on the MethComp package for R. The book has a strange flow, but could serve as a good reference once readers know what they are looking for.
Assessment: This book presents useful information about the complexities of method comparison studies specific to clinical/biomedical research. It is not necessarily written at an easily accessible level for graduate students, clinicians, or researchers who do not have a statistical background, but it would be helpful for those who have a background in biostatistics/epidemiology and conduct method comparison studies. There is some overlap with Statistical Evaluation of Measurement Errors: Design and Analysis of Reliability Studies, Dunn (Wiley-Blackwell, 2009), but this book focuses more on common situations that arise in clinical healthcare research. I would consider using it in a course intended for students seeking advanced degrees in biostatistics and epidemiology.
From the Publisher
"This book presents useful information about the complexities of method comparison studies specific to clinical/biomedical research. . . I would consider using it in a course intended for students seeking advanced degrees in biostatistics and epidemiology." (Doody's, 16 September 2011)

"In conclusion, this book provides a statistical modeling approach to the comparison of clinical measurements. The modeling aspects will be particularly appreciated by researchers and others mathematically sophisticated, while the computer code at the end of the book will be useful for practitioners wishing to implement the methods." (Journal of Biopharmaceutical Statistics, January 2011)

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Product Details

  • ISBN-13: 9780470694237
  • Publisher: Wiley
  • Publication date: 10/11/2010
  • Series: Statistics in Practice Series , #92
  • Edition number: 1
  • Pages: 172
  • Product dimensions: 6.10 (w) x 9.20 (h) x 0.60 (d)

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Table of Contents

Acknowledgments

1 Introduction 1

2 Method comparisons 5

2.1 One measurement by each method 5

2.1.1 Prediction of one method from another 8

2.1.2 Why not use the correlation? 8

2.1.3 A new method and a reference method 9

2.2 Replicate measurements by each method 10

2.2.1 Exchangeable replicates: fat data 10

2.2.2 Linked replicates: oximetry data 11

2.2.3 Why not use the averages of the replicates? 12

2.3 More than two methods 13

2.4 Terminology and notation 14

2.5 What it is all about 14

3 How to 17

3.1 ...use this chapter 17

3.2 Two methods 18

3.2.1 Single measurements 18

3.2.2 Comparing with a gold standard 18

3.2.3 Replicate measurements 19

3.3 More than two methods 19

3.3.1 Single measurements 20

3.3.2 Replicate measurements 20

4 Two methods with a single measurement on each 21

4.1 Model for limits of agreement 22

4.1.1 Prediction between methods 24

4.1.2 The correlation of the difference and the average 26

4.2 Non-constant difference between methods 27

4.3 A worked example 30

4.4 What really goes on 31

4.4.1 Scaling 31

4.4.2 Independence 32

4.4.3 Actual behavior 32

4.5 Other regression methods for non-constant bias 33

4.5.1 Why ordinary regression fails 33

4.5.2 Deming regression 34

4.6 Comparison with a gold standard 35

4.7 Non-constant variance 35

4.7.1 Regression approach 36

4.7.2 A worked example 40

4.8 Transformations 45

4.8.1 Log transformation 45

4.9 Summary 47

5 Replicate measurements 49

5.1 Pairing of replicate measurements 49

5.1.1 Exchangeable replicates 50

5.1.2 Linked replicates 53

5.2 Plotting replicate measurements 55

5.3 Models for replicate measurements 55

5.3.1 Exchangeable replicates 55

5.3.2 Linked replicates 57

5.4 Interpretation of the random effects 59

5.5 Estimation 61

5.6 Getting it wrong and getting it almost right 61

5.6.1 Averaging over replicates 62

5.6.2 Replicates as items 63

5.7 Summary 64

6 Several methods of measurement 67

6.1 Model 67

6.2 Replicate measurements 68

6.3 Single measurement by each method 69

7 A general model for method comparisons 71

7.1 Scaling 72

7.2 Interpretation of the random effects 73

7.3 Parametrization of the mean 74

7.4 Prediction limits 75

7.4.1 Mean of replicates 77

7.4.2 Plotting predictions between methods 77

7.4.3 Reporting variance components 77

7.4.4 Comparison with a gold standard 79

7.5 Estimation 80

7.5.1 Alternating regressions 80

7.5.2 Estimation using BUGS 85

7.5.3 A worked example 87

7.6 Models with non-constant variance 92

7.6.1 Linear dependence of residual standard error 93

7.7 Summary 96

8 Transformation of measurements 99

8.1 Log transformation 100

8.2 Transformations of percentages 100

8.2.1 A worked example 101

8.2.2 Implementation in MethComp 104

8.3 Other transformations 105

8.4 Several methods 105

8.5 Variance components 105

8.6 Summary 106

9 Repeatability, reproducibility and coefficient of variation 107

9.1 Repeatability 108

9.2 Reproducibility 109

9.3 Coefficient of variation 110

9.3.1 Symmetric interval on the log scale 112

9.3.2 Computing the CV correctly 113

9.3.3 Transformations 113

10 Measures of association and agreement 115

10.1 Individual bioequivalence criterion 116

10.2 Agreement index 118

10.3 Relative variance index 119

10.4 Total deviation index 120

10.5 Correlation measures 121

10.5.1 Correlation coefficient 122

10.5.2 Intraclass correlation coefficient 122

10.5.3 Concordance correlation coefficient 124

10.6 Summary 126

11 Design of method comparison studies 127

11.1 Sample size 128

11.1.1 Mean parameters 128

11.1.2 Variance parameters 128

11.2 Repeated measures designs 130

11.3 Summary 131

12 Examples using standard software 133

12.1 SAS 134

12.1.1 Exchangeable replicates 134

12.1.2 Linked replicates 136

12.2 Stata 137

12.2.1 Exchangeable replicates 137

12.2.2 Linked replicates 139

12.3 R 141

12.3.1 Exchangeable replicates 141

12.3.2 Linked replicates 143

13 The MethComp package for R 149

13.1 Data structures 149

13.2 Function overview 150

13.2.1 Graphical functions 150

13.2.2 Data manipulating functions 151

13.2.3 Analysis functions 151

13.2.4 Reporting functions 152

References 153

Index 155

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