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9780470023563

Applied Mixed Models in Medicine

by ;
  • ISBN13:

    9780470023563

  • ISBN10:

    0470023562

  • Edition: 2nd
  • Format: Hardcover
  • Copyright: 2006-05-18
  • Publisher: WILEY
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Summary

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 researchFully 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 optionsIncludes many more examples using real data, including examples from health research and epidemiologyIncludes a new section on missing data, and a serious update of the material on repeated measuresSupported by a Website featuring computer code, data sets, and further material

Author Biography

Helen Brown, Principal Statistician, NHS Scotland, Edinburgh, UK <p> <p> Robin Prescott, Medical Statistics Unit, University of Edinburgh Medical School, UK

Table of Contents

Preface.
Series Preface.
Mixed Models Notation.
1 Introduction.
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 About Mixed Models.
1.6 Some Useful Definitions.
2 Normal Mixed 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 Mixed Models (GLMMs).
3.1 Generalised Linear Models (GLMs).
3.2 Generalised Linear Mixed Models (GLMMs).
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 Repeated Measures 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 Mixed Models.
8.1 Trials with Repeated Measurements Within Visits.
8.2 Multi-Centre Trials with Repeated Measures.
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 Area Mortality Estimates.
8.12 Estimating Surgeon Performance.
8.13 Event History Analysis.
8.14 A laboratory study using a within subject 4x4 factorial design.
8.15 Bioequivalence studies with replicate crossover designs.
8.16 Cluster randomised trials.
9 Software for Fitting Mixed Models.
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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