A mixed model allows the incorporation of both fixed and randomvariables within a statistical analysis. This enables efficientinferences and more information to be gained from the data. Theapplication of mixed models is an increasingly popular way ofanalysing medical data, particularly in the pharmaceuticalindustry. There have been many recent advances in mixed modelling,particularly regarding the software and applications. This newedition of a groundbreaking text discusses the latest developments,from updated SAS techniques to the increasingly wide range ofapplications.* Presents an overview of the theory and applications of mixedmodels in medical research, including the latest developments andnew sections on bioequivalence, cluster randomised trials andmissing data.* Easily accessible to practitioners in any area where mixedmodels are used, including medical statisticians andeconomists.* Includes numerous examples using real data from medical andhealth research, and epidemiology, illustrated with SAS code andoutput.* Features new version of SAS, including the procedure PROCGLIMMIX and an introduction to other available software.* Supported by a website featuring computer code, data sets, andfurther material, available at:chs.med.ed.ac.uk/phs/mixed/.This much-anticipated second edition is ideal for appliedstatisticians working in medical research and the pharmaceuticalindustry, as well as teachers and students of statistics courses inmixed models. The text will also be of great value to a broad rangeof scientists, particularly those working the medical andpharmaceutical areas.

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 TrialDesign.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 SimpleBetween-Patient Trial.8.7 Estimating Variance Components in an Animal PhysiologyTrial.8.8 Inter- and Intra-Observer Variation in Foetal ScanMeasurements.8.9 Components of Variation and Mean Estimates in a CardiologyExperiment.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 × 4Factorial Design.8.15 Bioequivalence Studies with Replicate Cross-OverDesigns.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.