Applied Mixed Models in Medicine / Edition 2

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Overview

A mixed model allows the incorporation of both fixed and random variables within a statistical analysis. This enables efficient inferences and more information to be gained from the data. The application of mixed models is an increasingly popular way of analysing medical data, particularly in the pharmaceutical industry. There have been many recent advances in mixed modelling, particularly regarding the software and applications. This new edition of a groundbreaking text discusses the latest developments, from updated SAS techniques to the increasingly wide range of applications.

  • Presents an overview of the theory and applications of mixed models in medical research, including the latest developments and new sections on bioequivalence, cluster randomised trials and missing data.
  • Easily accessible to practitioners in any area where mixed models are used, including medical statisticians and economists.
  • Includes numerous examples using real data from medical and health research, and epidemiology, illustrated with SAS code and output.
  • Features new version of SAS, including the procedure PROC GLIMMIX and an introduction to other available software.
  • Supported by a website featuring computer code, data sets, and further material, available at: http://www.chs.med.ed.ac.uk/phs/mixed/.

This much-anticipated second edition is ideal for applied statisticians working in medical research and the pharmaceutical industry, as well as teachers and students of statistics courses in mixed models. The text will also be of great value to a broad range of scientists, particularly those working the medical and pharmaceutical areas.

Disc. problems in use of mixed models; when conventional, fixed effect models might be prefered; theory; software.

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Editorial Reviews

From the Publisher
"…a valuable mixed model resource for most applied statisticians working in the medical environment." (Biometrics, June 2007)

"…useful for practitioners and applied statisticians working in medical science." (Journal of the American Statistical Association, September 2007)

"…takes a practical rather than theoretical approach and requires understanding of only basic statistics." (MAA Reviews, October 30, 2006)

“This second edition gives an overview of the theory of mixed models and its application to real data in medical research.” (Zentralblatt MATH, April 2007)

Doody's Review Service
Reviewer: Sharon M. Homan, PhD (Kansas Health Institute)
Description: Mixed models, also known as multilevel models in the social sciences, allow both fixed and random variables within a statistical analysis. The general linear model assumption that the error terms are independent and identically distributed is relaxed in mixed models, so that observations can be correlated (e.g., repeated measures, cross-over trial, etc.). This second edition describes current methods and advanced SAS techniques for applying mixed models. The first edition was published in 1999.
Purpose: The authors present the theory and application of mixed models in medical research, including the latest developments in bioequivalence, cross-over trials, and cluster randomized trials. Their purpose is to make mixed modeling easily accessible to practitioners such as medical statisticians and economists. The book is well written, thorough, and highly applicable. The examples, complete with SAS code and output, are outstanding. The authors meet their objectives.
Audience: This is written for applied statisticians working in medical research and the pharmaceutical industry, as well as teachers and students of statistics courses in mixed models. The authors are experienced statisticians and highly credible scholars from the U.K. The book is part of the Statistics in Practice international series of books that provide statistical support for professionals and researchers.
Features: Beginning by describing the capabilities of mixed models, the authors introduce readers to the general linear model for fitting normally distributed data, and then extend the general linear model to general linear mixed models. The authors then examine how mixed models can be applied with categorical outcome variables. Chapters 5 to 7 are devoted to practical application of mixed models using particular designs. Chapter 8 includes a new section on bioequivalence studies and cluster randomized trials. Chapter 9 concludes by describing software options, including SAS and the PROC GLIMIX and PROC GENMOD procedures. The reference pages on mixed model notation, the glossary of terms, and the detailed SAS programming code and annotation greatly enhance the book's usefulness.
Assessment: This is an excellent resource for biostatisticians and medical researchers. It provides the reader with a thorough understanding of the concepts of mixed models. There are many social science texts on mixed modeling (also called multi-level modeling) but few that clearly link mixed models to clinical research designs. The second edition uses the 9th version of SAS and expands the coverage of categorical outcomes.
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Product Details

  • ISBN-13: 9780470023563
  • Publisher: Wiley
  • Publication date: 6/14/2006
  • Series: Statistics in Practice Series , #28
  • Edition description: REV
  • Edition number: 2
  • Pages: 478
  • Sales rank: 1,286,867
  • Product dimensions: 6.40 (w) x 9.23 (h) x 1.23 (d)

Meet the Author

Helen Brown, Principal Statistician, NHS Scotland, Edinburgh, UK

Robin Prescott, Medical Statistics Unit, University of Edinburgh Medical School, UK

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Table of Contents

Preface to Second Edition.

Mixed Model Notations.

1 Introduction.

1.1 The Use of Mixed Models.

1.2 Introductory Example.

1.3 A Multi-Centre Hypertension Trial.

1.4 Repeated Measures Data.

1.5 More aboutMixed Models.

1.6 Some Useful Definitions.

2 NormalMixed Models.

2.1 Model Definition.

2.2 Model Fitting Methods.

2.3 The Bayesian Approach.

2.4 Practical Application and Interpretation.

2.5 Example.

3 Generalised Linear MixedModels.

3.1 Generalised Linear Models.

3.2 Generalised Linear Mixed Models.

3.3 Practical Application and Interpretation.

3.4 Example.

4 Mixed Models for Categorical Data.

4.1 Ordinal Logistic Regression (Fixed Effects Model).

4.2 Mixed Ordinal Logistic Regression.

4.3 Mixed Models for Unordered Categorical Data.

4.4 Practical Application and Interpretation.

4.5 Example.

5 Multi-Centre Trials and Meta-Analyses.

5.1 Introduction to Multi-Centre Trials.

5.2 The Implications of using Different Analysis Models.

5.3 Example: A Multi-Centre Trial.

5.4 Practical Application and Interpretation.

5.5 Sample Size Estimation.

5.6 Meta-Analysis.

5.7 Example: Meta-analysis.

6 RepeatedMeasures Data.

6.1 Introduction.

6.2 Covariance Pattern Models.

6.3 Example: Covariance Pattern Models for Normal Data.

6.4 Example: Covariance Pattern Models for Count Data.

6.5 Random Coefficients Models.

6.6 Examples of Random Coefficients Models.

6.7 Sample Size Estimation.

7 Cross-Over Trials.

7.1 Introduction.

7.2 Advantages of Mixed Models in Cross-Over Trials.

7.3 The AB/BA Cross-Over Trial.

7.4 Higher Order Complete Block Designs.

7.5 Incomplete Block Designs.

7.6 Optimal Designs.

7.7 Covariance Pattern Models.

7.8 Analysis of Binary Data.

7.9 Analysis of Categorical Data.

7.10 Use of Results from Random Effects Models in Trial Design.

7.11 General Points.

8 Other Applications of MixedModels.

8.1 Trials with Repeated Measurements within Visits.

8.2 Multi-Centre Trials with Repeated Measurements.

8.3 Multi-Centre Cross-Over Trials.

8.4 Hierarchical Multi-Centre Trials and Meta-Analysis.

8.5 Matched Case–Control Studies.

8.6 Different Variances for Treatment Groups in a Simple Between-Patient Trial.

8.7 Estimating Variance Components in an Animal Physiology Trial.

8.8 Inter- and Intra-Observer Variation in Foetal Scan Measurements.

8.9 Components of Variation and Mean Estimates in a Cardiology Experiment.

8.10 Cluster Sample Surveys.

8.11 Small AreaMortality Estimates.

8.12 Estimating Surgeon Performance.

8.13 Event History Analysis.

8.14 A Laboratory Study Using aWithin-Subject 4 × 4 Factorial Design.

8.15 Bioequivalence Studies with Replicate Cross-Over Designs.

8.16 Cluster Randomised Trials.

9 Software for Fitting MixedModels.

9.1 Packages for Fitting Mixed Models.

9.2 Basic use of PROC MIXED.

9.3 Using SAS to Fit Mixed Models to Non-Normal Data.

Glossary.

References.

Index.

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