Helen Brown & Robin Prescott 
Applied Mixed Models in Medicine 

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.

Table of Content

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 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.

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 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 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 Area Mortality Estimates.

8.12 Estimating Surgeon Performance.

8.13 Event History Analysis.

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

8.15 Bioequivalence Studies with Replicate Cross-Over
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.

About the author

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

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