Comparing Clinical Measurement Methods: A Practical Guide

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.


Spis treści:
1 Introduction
2 Method comparisons
2.1 One measurement by each method . . . . . . . . . . . . . . . . . . .
2.2 Replicate measurements by each method . . . . . . . . . . . . . . . .
2.3 More than two methods . . . . . . . . . . . . . . . . . . . . . . . . .
2.4 Terminology and notation . . . . . . . . . . . . . . . . . . . . . . . .
2.5 What it is all about . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3 How to. . .
3.1 . . . use this chapter . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.2 Two methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.3 More than two methods . . . . . . . . . . . . . . . . . . . . . . . . .
4 Two methods with a single measurement on each
4.1 Model for limits of agreement . . . . . . . . . . . . . . . . . . . . .
4.2 Non–constant difference between methods . . . . . . . . . . . . . . . .
4.3 A worked example . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.4 What really goes on . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.5 Other regression methods for non–constant bias . . . . . . . . . . . .
4.6 Comparison with a gold standard . . . . . . . . . . . . . . . . . . . .
4.7 Non–constant variance . . . . . . . . . . . . . . . . . . . . . . . . . .
4.8 Transformations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.9 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
5 Replicate measurements
5.1 Pairing of replicate measurements . . . . . . . . . . . . . . . . . . . .
5.2 Plotting replicate measurements . . . . . . . . . . . . . . . . . . . . .
5.3 Models for replicate measurements . . . . . . . . . . . . . . . . . . .
5.4 Interpretation of the random effects . . . . . . . . . . . . . . . . . . .
5.5 Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
5.6 Getting it wrong and getting it almost right . . . . . . . . . . . . . .
5.7 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
6 Several methods of measurement
6.1 Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
6.2 Replicate measurements . . . . . . . . . . . . . . . . . . . . . . . . .
6.3 Single measurement by each method . . . . . . . . . . . . . . . . . .
7 A general model for method comparisons
7.1 Scaling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
7.2 Interpretation of the random effects . . . . . . . . . . . . . . . . . . .
7.3 Parametrization of the mean . . . . . . . . . . . . . . . . . . . . . . .
7.4 Prediction limits . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
7.5 Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
7.6 Models with non–constant variance . . . . . . . . . . . . . . . . . . .
7.7 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
8 Transformation of measurements.......................
8.1 Log–transformation . . . . . . . . . . . . . . . . . . . . . . . . . . . .
8.2 Transformations of percentages . . . . . . . . . . . . . . . . . . . . .
8.3 Other transformations . . . . . . . . . . . . . . . . . . . . . . . . . .
8.4 Several methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
8.5 Variance components . . . . . . . . . . . . . . . . . . . . . . . . . . .
8.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
9 Repeatability, reproducibility and coefficient of variation
9.1 Repeatability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
9.2 Reproducibility . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
9.3 Coefficient of variation . . . . . . . . . . . . . . . . . . . . . . . . . .
10 Measures of association and agreement
10.1 IBC | individual bioequivalence criterion . . . . . . . . . . . . . . .
10.2 Agreement index . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
10.3 Relative variance index . . . . . . . . . . . . . . . . . . . . . . . . . .
10.4 Total deviation index . . . . . . . . . . . . . . . . . . . . . . . . . . .
10.5 Correlation measures . . . . . . . . . . . . . . . . . . . . . . . . . . .
10.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
11 Design of method comparison s tudies
11.1 Sample size . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
11.2 Repeated measures designs . . . . . . . . . . . . . . . . . . . . . . . .
11.3 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
12 Examples using standard software
12.1 SAS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
12.2 Stata . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
12.3 R . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
13 The MethComp package for R
13.1 Data structures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
13.2 Function overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Bibliography
Index


Okładka tylna:
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.