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Applied Mixed Models in Medicine

ISBN-10: 0470023562

ISBN-13: 9780470023563

Edition: 2nd 2006 (Revised)

Authors: Helen Brown, Robin Prescott

List price: $141.00
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Description:

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…    
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Book details

List price: $141.00
Edition: 2nd
Copyright year: 2006
Publisher: John Wiley & Sons, Incorporated
Publication date: 5/18/2006
Binding: Hardcover
Pages: 478
Size: 6.00" wide x 9.25" long x 1.25" tall
Weight: 1.738
Language: English

Helen Brown, born in 1954, is one of New Zealand's widest read, longest running columnists and a best-selling author. In 1991 she was awarded a Nuffield Press Fellowship to Cambridge University, UK. Helen has written nine books including: Florascope, In Deep, Clouds of Happiness, Tomorrow When It's Summer, and A Slice of Banana Cake. Her most recent book, Cleo, was released to wide acclaim landing on the New York Times Best Seller List its first week in bookstores. Before moving with her husband Philip and three children to Melbourne nine years ago Helen was a full time feature writer for the Sunday Star Times in Auckland.

Preface to Second Edition
Mixed Model Notations
Introduction
The Use of Mixed Models
Introductory Example
Simple model to assess the effects of treatment (Model A)
A model taking patient effects into account (Model B)
Random effects model (Model C)
Estimation (or prediction) of random effects
A Multi-Centre Hypertension Trial
Modelling the data
Including a baseline covariate (Model B)
Modelling centre effects (Model C)
Including centre-by-treatment interaction effects (Model D)
Modelling centre and centre-treatment effects as random (Model E)
Repeated Measures Data
Covariance pattern models
Random coefficients models
More about Mixed Models
What is a mixed model
Why use mixed models
Communicating results
Mixed models in medicine
Mixed models in perspective
Some Useful Definitions
Containment
Balance
Error strata
Normal Mixed Models
Model Definition
The fixed effects model
The mixed model
The random effects model covariance structure
The random coefficients model covariance structure
The covariance pattern model covariance structure
Model Fitting Methods
The likelihood function and approaches to its maximisation
Estimation of fixed effects
Estimation (or prediction) of random effects and coefficients
Estimation of variance parameters
The Bayesian Approach
Introduction
Determining the posterior density
Parameter estimation, probability intervals and p-values
Specifying non-informative prior distributions
Evaluating the posterior distribution
Practical Application and Interpretation
Negative variance components
Accuracy of variance parameters
Bias in fixed and random effects standard errors
Significance testing
Confidence intervals
Model checking
Missing data
Example
Analysis models
Results
Discussion of points from Section 2.4
Generalised Linear Mixed Models
Generalised Linear Models
Introduction
Distributions
The general form for exponential distributions
The GLM definition
Fitting the GLM
Expressing individual distributions in the general exponential form
Conditional logistic regression
Generalised Linear Mixed Models
The GLMM definition
The likelihood and quasi-likelihood functions
Fitting the GLMM
Practical Application and Interpretation
Specifying binary data
Uniform effects categories
Negative variance components
Fixed and random effects estimates
Accuracy of variance parameters and random effects shrinkage
Bias in fixed and random effects standard errors
The dispersion parameter
Significance testing
Confidence intervals
Model checking
Example
Introduction and models fitted
Results
Discussion of points from Section 3.3
Mixed Models for Categorical Data
Ordinal Logistic Regression (Fixed Effects Model)
Mixed Ordinal Logistic Regression
Definition of the mixed ordinal logistic regression model
Residual variance matrix
Alternative specification for random effects models
Likelihood and quasi-likelihood functions
Model fitting methods
Mixed Models for Unordered Categorical Data
The G matrix
The R matrix
Fitting the model
Practical Application and Interpretation
Expressing fixed and random effects results
The proportional odds assumption
Number of covariance parameters
Choosing a covariance pattern
Interpreting covariance parameters
Checking model assumptions
The dispersion parameter
Other points
Example
Multi-Centre Trials and Meta-Analyses
Introduction to Multi-Centre Trials
What is a multi-centre trial?
Why use mixed models to analyse multi-centre data?
The Implications of using Different Analysis Models
Centre and centre-treatment effects fixed
Centre effects fixed, centre-treatment effects omitted
Centre and centre treatment effects random
Centre effects random, centre-treatment effects omitted
Example: A Multi-Centre Trial
Practical Application and Interpretation
Plausibility of a centre-treatment interaction
Generalisation
Number of centres
Centre size
Negative variance components
Balance
Sample Size Estimation
Normal data
Non-normal data
Meta-Analysis
Example: Meta-analysis
Analyses
Results
Treatment estimates in individual trials
Repeated Measures Data
Introduction
Reasons for repeated measurements
Analysis objectives
Fixed effects approaches
Mixed models approaches
Covariance Pattern Models
Covariance patterns
Choice of covariance pattern
Choice of fixed effects
General points
Example: Covariance Pattern Models for Normal Data
Analysis models
Selection of covariance pattern
Assessing fixed effects
Model checking
Example: Covariance Pattern Models for Count Data
Analysis models
Analysis using a categorical mixed model
Random Coefficients Models
Introduction
General points
Comparisons with fixed effects approaches
Examples of Random Coefficients Models
A linear random coefficients model
A polynomial random coefficients model
Sample Size Estimation
Normal data
Non-normal data
Categorical data
Cross-Over Trials
Introduction
Advantages of Mixed Models in Cross-Over Trials
The AB/BA Cross-Over Trial
Example: AB/BA cross-over design
Higher Order Complete Block Designs
Inclusion of carry-over effects
Example: four-period, four-treatment cross-over trial
Incomplete Block Designs
The three-treatment, two-period design (Koch's design)
Example: two-period cross-over trial
Optimal Designs
Example: Balaam's design
Covariance Pattern Models
Structured by period
Structured by treatment
Example: four-way cross-over trial
Analysis of Binary Data
Analysis of Categorical Data
Use of Results from Random Effects Models in Trial Design
Example
General Points
Other Applications of Mixed Models
Trials with Repeated Measurements within Visits
Covariance pattern models
Example
Random coefficients models
Example: random coefficients models
Multi-Centre Trials with Repeated Measurements
Example: multi-centre hypertension trial
Covariance pattern models
Multi-Centre Cross-Over Trials
Hierarchical Multi-Centre Trials and Meta-Analysis
Matched Case-Control Studies
Example
Analysis of a quantitative variable
Check of model assumptions
Analysis of binary variables
Different Variances for Treatment Groups in a Simple Between-Patient Trial
Example
Estimating Variance Components in an Animal Physiology Trial
Sample size estimation for a future experiment
Inter- and Intra-Observer Variation in Foetal Scan Measurements
Components of Variation and Mean Estimates in a Cardiology Experiment
Cluster Sample Surveys
Example: cluster sample survey
Small Area Mortality Estimates
Estimating Surgeon Performance
Event History Analysis
Example
A Laboratory Study Using a Within-Subject 4 x 4 Factorial Design
Bioequivalence Studies with Replicate Cross-Over Designs
Example
Cluster Randomised Trials
Example: a trial to evaluate integrated care pathways for treatment of children with asthma in hospital
Example: Edinburgh randomised trial of breast screening
Software for Fitting Mixed Models
Packages for Fitting Mixed Models
Basic use of PROC Mixed
Using SAS to Fit Mixed Models to Non-Normal Data
PROC GLIMMIX
PROC GENMOD
Glossary
References
Index
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