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El. knyga: Comparing Clinical Measurement Methods: A Practical Guide

(University of Copenhagen)
  • Formatas: PDF+DRM
  • Serija: Statistics in Practice
  • Išleidimo metai: 17-Jun-2010
  • Leidėjas: John Wiley & Sons Inc
  • Kalba: eng
  • ISBN-13: 9780470683002
Kitos knygos pagal šią temą:
Comparing Clinical Measurement Methods: A Practical GuideDidesnis paveikslėlis
  • Formatas: PDF+DRM
  • Serija: Statistics in Practice
  • Išleidimo metai: 17-Jun-2010
  • Leidėjas: John Wiley & Sons Inc
  • Kalba: eng
  • ISBN-13: 9780470683002
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Highly practical, this work explores techniques for comparing statistical measurement methods in clinical trials. One of its key aims is to explicitly define a model for the data under consideration, thereby allowing the establishment of conversion equations between methods. A variety of special cases are explored, followed by the exposition of a general model in chapter seven. Almost every chapter ends with a brief summary that concludes with instructions to "... proceed as follows:" through four to six steps. It contains a reference to a website containing the MethComp package, along with examples and illustrative programs. The site also supplies exercises with solutions making the work useful as a textbook. Annotation ©2010 Book News, Inc., Portland, OR (booknews.com)

It is difficult for medics and biostatisticians comparing two different methods of measuring to ascertain if either method is giving the true value of the quantity being measured. Comparing Clinical Measurement Methods provides the practical tools for analyzing method comparison studies along with guidance on what to report and how to plan comparison studies. Author Bendix Carstensen, an esteemed expert on the subject, presents a modeling framework that allows biostatisticians, clinicians, medical researchers, and practitioners to analyze clinical data and compare measurements taken from different clinical centers using different methods.
  • Presents a modeling framework in which biostatisticians can analyze clinical data and compare measurements taken from different clinical centres using different methods.
  • Provides the practical tools for analyzing method comparison studies along with guidance on what to report and how to plan comparison studies.
  • Explores the benefits of different measurement methods and provides advice on the use of appropriate software, illustrated throughout with computer examples in SAS, Stata and R.

Recenzijos

"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)

Acknowledgments xi
1 Introduction
1(4)
2 Method comparisons
5(12)
2.1 One measurement by each method
5(5)
2.1.1 Prediction of one method from another
8(1)
2.1.2 Why not use the correlation?
8(1)
2.1.3 A new method and a reference method
9(1)
2.2 Replicate measurements by each method
10(3)
2.2.1 Exchangeable replicates: fat data
10(1)
2.2.2 Linked replicates: oximetry data
11(1)
2.2.3 Why not use the averages of the replicates?
12(1)
2.3 More than two methods
13(1)
2.4 Terminology and notation
14(1)
2.5 What it is all about
14(3)
3 How to
17(4)
3.1 ...use this chapter
17(1)
3.2 Two methods
18(1)
3.2.1 Single measurements
18(1)
3.2.2 Comparing with a gold standard
18(1)
3.2.3 Replicate measurements
19(1)
3.3 More than two methods
19(2)
3.3.1 Single measurements
20(1)
3.3.2 Replicate measurements
20(1)
4 Two methods with a single measurement on each
21(28)
4.1 Model for limits of agreement
22(5)
4.1.1 Prediction between methods
24(2)
4.1.2 The correlation of the difference and the average
26(1)
4.2 Non-constant difference between methods
27(3)
4.3 A worked example
30(1)
4.4 What really goes on
31(2)
4.4.1 Scaling
31(1)
4.4.2 Independence
32(1)
4.4.3 Actual behavior
32(1)
4.5 Other regression methods for non-constant bias
33(2)
4.5.1 Why ordinary regression fails
33(1)
4.5.2 Deming regression
34(1)
4.6 Comparison with a gold standard
35(1)
4.7 Non-constant variance
35(10)
4.7.1 Regression approach
36(4)
4.7.2 A worked example
40(5)
4.8 Transformations
45(2)
4.8.1 Log transformation
45(2)
4.9 Summary
47(2)
5 Replicate measurements
49(18)
5.1 Pairing of replicate measurements
49(6)
5.1.1 Exchangeable replicates
50(3)
5.1.2 Linked replicates
53(2)
5.2 Plotting replicate measurements
55(1)
5.3 Models for replicate measurements
55(4)
5.3.1 Exchangeable replicates
55(2)
5.3.2 Linked replicates
57(2)
5.4 Interpretation of the random effects
59(2)
5.5 Estimation
61(1)
5.6 Getting it wrong and getting it almost right
61(3)
5.6.1 Averaging over replicates
62(1)
5.6.2 Replicates as items
63(1)
5.7 Summary
64(3)
6 Several methods of measurement
67(4)
6.1 Model
67(1)
6.2 Replicate measurements
68(1)
6.3 Single measurement by each method
69(2)
7 A general model for method comparisons
71(28)
7.1 Scaling
72(1)
7.2 Interpretation of the random effects
73(1)
7.3 Parametrization of the mean
74(1)
7.4 Prediction limits
75(5)
7.4.1 Mean of replicates
77(1)
7.4.2 Plotting predictions between methods
77(1)
7.4.3 Reporting variance components
77(2)
7.4.4 Comparison with a gold standard
79(1)
7.5 Estimation
80(12)
7.5.1 Alternating regressions
80(5)
7.5.2 Estimation using BUGS
85(2)
7.5.3 A worked example
87(5)
7.6 Models with non-constant variance
92(4)
7.6.1 Linear dependence of residual standard error
93(3)
7.7 Summary
96(3)
8 Transformation of measurements
99(8)
8.1 Log transformation
100(1)
8.2 Transformations of percentages
100(5)
8.2.1 A worked example
101(3)
8.2.2 Implementation in MethComp
104(1)
8.3 Other transformations
105(1)
8.4 Several methods
105(1)
8.5 Variance components
105(1)
8.6 Summary
106(1)
9 Repeatability, reproducibility and coefficient of variation
107(8)
9.1 Repeatability
108(1)
9.2 Reproducibility
109(1)
9.3 Coefficient of variation
110(5)
9.3.1 Symmetric interval on the log scale
112(1)
9.3.2 Computing the CV correctly
113(1)
9.3.3 Transformations
113(2)
10 Measures of association and agreement
115(12)
10.1 Individual bioequivalence criterion
116(2)
10.2 Agreement index
118(1)
10.3 Relative variance index
119(1)
10.4 Total deviation index
120(1)
10.5 Correlation measures
121(5)
10.5.1 Correlation coefficient
122(1)
10.5.2 Intraclass correlation coefficient
122(2)
10.5.3 Concordance correlation coefficient
124(2)
10.6 Summary
126(1)
11 Design of method comparison studies
127(6)
11.1 Sample size
128(2)
11.1.1 Mean parameters
128(1)
11.1.2 Variance parameters
128(2)
11.2 Repeated measures designs
130(1)
11.3 Summary
131(2)
12 Examples using standard software
133(16)
12.1 SAS
134(3)
12.1.1 Exchangeable replicates
134(2)
12.1.2 Linked replicates
136(1)
12.2 Stata
137(4)
12.2.1 Exchangeable replicates
137(2)
12.2.2 Linked replicates
139(2)
12.3 R
141(8)
12.3.1 Exchangeable replicates
141(2)
12.3.2 Linked replicates
143(6)
13 The MethComp package for R
149(4)
13.1 Data structures
149(1)
13.2 Function overview
150(3)
13.2.1 Graphical functions
150(1)
13.2.2 Data manipulating functions
151(1)
13.2.3 Analysis functions
151(1)
13.2.4 Reporting functions
152(1)
References 153(2)
Index 155
Bendix Carstensen, Senior Statistician at Steno Diabetes Center, Denmark; also of Department of Biostatistics, Copenhagen, Denmark Bendix Carstensen has been working as a biostatistician in research institutions since 1983. During the last 10 years he has taught courses for medical PhD-students at the Department of Biostatistics at the University of Copenhagen and has been involved in the delivery of overseas courses in epidemiology and the comparison of measurement methods.
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