Applied Mixed Models in Medicine

A fully updated edition of this key text on mixed models, focusing on applications in medical research The application of mixed models is an increasingly popular way of analysing medical data, particularly in the pharmaceutical industry. A mixed model allows the incorporation of both fixed and random variables within a statistical analysis, enabling efficient inferences and more information to be gained from the data. There have been many recent advances in mixed modelling, particularly regarding the software and applications. This third edition of Brown and Prescott s groundbreaking text provides an update on the latest developments, and includes guidance on the use of current SAS techniques across a 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 incomplete block designs and the analysis of bilateral 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 the new version of SAS, including new graphics for model diagnostics and the procedure PROC MCMC. Supported by a website featuring computer code, data sets, and further material. This third edition will appeal to applied statisticians working in medical research and the pharmaceutical industry, as well as teachers and students of statistics courses in mixed models. The book will also be of great value to a broad range of scientists, particularly those working in the medical and pharmaceutical areas.

A fully updated edition of this key text on mixed models, focusing on applications in medical researchThe application of mixed models is an increasingly popular way of analysing medical data, particularly in the pharmaceutical industry. A mixed model allows the incorporation of both fixed and random variables within a statistical analysis, enabling efficient inferences and more information to be gained from the data. There have been many recent advances in mixed modelling, particularly regarding the software and applications. This third edition of Brown and Prescott's groundbreaking text provides an update on the latest developments, and includes guidance on the use of current SAS techniques across a 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 incomplete block designs and the analysis of bilateral 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 the new version of SAS, including new graphics for model diagnostics and the procedure PROC MCMC.* Supported by a website featuring computer code, data sets, and further material.This third edition will appeal to applied statisticians working in medical research and the pharmaceutical industry, as well as teachers and students of statistics courses in mixed models. The book will also be of great value to a broad range of scientists, particularly those working in the medical and pharmaceutical areas.

Preface to Second Edition xiiiMixed Model Notation xvii1 Introduction 11.1 The Use of Mixed Models 11.2 Introductory Example 31.3 A Multi-Centre Hypertension Trial 121.4 Repeated Measures Data 181.5 More about Mixed Models 221.6 Some Useful Definitions 272 Normal Mixed Models 332.1 Model Definition 332.2 Model Fitting Methods 452.3 The Bayesian Approach 562.4 Practical Application and Interpretation 702.5 Example 833 Generalised Linear Mixed Models 1073.1 Generalised Linear Models 1083.2 Generalised Linear Mixed Models 1203.3 Practical Application and Interpretation 1283.4 Example 1374 Mixed Models for Categorical Data 1534.1 Ordinal Logistic Regression (Fixed Effects Model) 1534.2 Mixed Ordinal Logistic Regression 1584.3 Mixed Models for Unordered Categorical Data 1634.4 Practical Application and Interpretation 1664.5 Example 1695 Multi-Centre Trials and Meta-Analyses 1835.1 Introduction to Multi-Centre Trials 1835.2 The Implications of using Different Analysis Models 1845.3 Example: A Multi-Centre Trial 1885.4 Practical Application and Interpretation 1955.5 Sample Size Estimation 1975.6 Meta-Analysis 2035.7 Example: Meta-analysis 2046 Repeated Measures Data 2156.1 Introduction 2156.2 Covariance Pattern Models 2186.3 Example: Covariance Pattern Models for Normal Data 2286.4 Example: Covariance Pattern Models for Count Data 2376.5 Random Coefficients Models 2456.6 Examples of Random Coefficients Models 2496.7 Sample Size Estimation 2677 Cross-Over Trials 2717.1 Introduction 2717.2 Advantages of Mixed Models in Cross-Over Trials 2727.3 The AB/BA Cross-Over Trial 2727.4 Higher Order Complete Block Designs 2797.5 Incomplete Block Designs 2847.6 Optimal Designs 2877.7 Covariance Pattern Models 2907.8 Analysis of Binary Data 2997.9 Analysis of Categorical Data 3037.10 Use of Results from Random Effects Models in Trial Design3077.11 General Points 3088 Other Applications of Mixed Models 3118.1 Trials with Repeated Measurements within Visits 3118.2 Multi-Centre Trials with Repeated Measurements 3308.3 Multi-Centre Cross-Over Trials 3378.4 Hierarchical Multi-Centre Trials and Meta-Analysis 3388.5 Matched Case-Control Studies 3398.6 Different Variances for Treatment Groups in a SimpleBetween-Patient Trial 3518.7 Estimating Variance Components in an Animal Physiology Trial3558.8 Inter- and Intra-Observer Variation in Foetal ScanMeasurements 3618.9 Components of Variation and Mean Estimates in a CardiologyExperiment 3638.10 Cluster Sample Surveys 3658.11 Small Area Mortality Estimates 3678.12 Estimating Surgeon Performance 3718.13 Event History Analysis 3728.14 A Laboratory Study Using a Within-Subject FactorialDesign 3758.15 Bioequivalence Studies with Replicate Cross-Over Designs3788.16 Cluster Randomised Trials 3928.17 Analysis of Bilateral Data xxx8.18 Incomplete Block Designs xxx9 Software for Fitting Mixed Models 4019.1 Packages for Fitting Mixed Models 4019.2 PROC MIXED 4039.3 Using SAS to Fit Mixed Models to Non-Normal Data 4239.4 PROC MCMC xxxGlossary 431References 435Index 441