Applied Mixed Models in Medicine, 2/e

Helen Brown, Robin Prescott

  • 出版商: Wiley
  • 出版日期: 2006-06-01
  • 售價: $1,750
  • 貴賓價: 9.8$1,715
  • 語言: 英文
  • 頁數: 478
  • 裝訂: Hardcover
  • ISBN: 0470023562
  • ISBN-13: 9780470023563
  • 下單後立即進貨 (約5~7天)




Since the publication of the first edition the topic of mixed modelling has seen many developments, particularly regarding software and applications. There are now many more software options for applying mixed model methodology, and SAS has been updated to include powerful new techniques. Applications of mixed models have increased, notably in the areas of health research and epidemiology.

This new edition presents:

  • Presents an overview of the theory of mixed models applied to problems in medical research
  • Fully updated to include up-to-date references and developments.
    Computer examples updated to the latest edition of SAS, and now includes more discussion of other software options
  • Includes many more examples using real data, including examples from health research and epidemiology
  • Includes a new section on missing data, and a serious update of the material on repeated measures
  • Supported by a Website featuring computer code, data sets, and further material


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.