Helen Brown, Robin Prescott

Applied Mixed Models in Medicine 2e

• Gebundenes Buch

Produktbeschreibung

Dieser Band beschäftigt sich mit kombinierten Modellen, einem bekannten statistischen Analyseverfahren, das häufig im Bereich multivariater Daten Anwendung findet. Hier werden die verschiedenen Modellarten sowie deren spezifische Anwendungsprobleme detailliert beschrieben und diskutiert. Die Autoren geben darüber hinaus Hinweise, wann konventionellere Modelle bevorzugt werden sollten. Verständlich geschrieben und mit umfangreichem Beispielmaterial. 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.

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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.

Produktdetails

• Verlag: Wiley & Sons
• Artikelnr. des Verlages: 14502356000
• 2. Aufl.
• Seitenzahl: 480
• Erscheinungstermin: 1. Juni 2006
• Englisch
• Abmessung: 235mm x 157mm x 32mm
• Gewicht: 795g
• ISBN-13: 9780470023563
• ISBN-10: 0470023562
• Artikelnr.: 20805118

Autorenporträt

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

Inhaltsangabe

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. Glossary.
References. Index.

Rezensionen

" easy to read, interesting, and informative " ( Journal of Biopharmaceutical Statistics , Vol. 17, Issue 4, 2007) "This second edition gives an overview of the theory of mixed models and its application to data in medical research." ( Zentralblatt MATH, April 2007) " takes a practical rather than theoretical approach and requires understanding of only basic statistics." ( MAA Reviews , October 30, 2006)