2nd Edition

Modelling Binary Data





ISBN 9781584883241
Published September 25, 2002 by Chapman and Hall/CRC
408 Pages 54 B/W Illustrations

USD $115.00

Prices & shipping based on shipping country


Preview

Book Description

Since the original publication of the bestselling Modelling Binary Data, a number of important methodological and computational developments have emerged, accompanied by the steady growth of statistical computing. Mixed models for binary data analysis and procedures that lead to an exact version of logistic regression form valuable additions to the statistician's toolbox, and author Dave Collett has fully updated his popular treatise to incorporate these important advances.

Modelling Binary Data, Second Edition now provides an even more comprehensive and practical guide to statistical methods for analyzing binary data. Along with thorough revisions to the original material-now independent of any particular software package- it includes a new chapter introducing mixed models for binary data analysis and another on exact methods for modelling binary data. The author has also added material on modelling ordered categorical data and provides a summary of the leading software packages.

All of the data sets used in the book are available for download from the Internet, and the appendices include additional data sets useful as exercises.

Table of Contents

INTRODUCTION
Some Examples
The Scope of this Book
Use of Statistical Software
STATISTICAL INFERENCE FOR BINARY DATA
The Binomial Distribution
Inference about the Success Probability
Comparison of Two Proportions
Comparison of Two or More Proportions
MODELS FOR BINARY AND BINOMIAL DATA
Statistical Modelling
Linear Models
Methods of Estimation
Fitting Linear Models to Binomial Data
Models for Binomial Response Data
The Linear Logistic Model
Fitting the Linear Logistic Model to Binomial Data
Goodness of Fit of a Linear Logistic Model
Comparing Linear Logistic Models
Linear Trend in Proportions
Comparing Stimulus-Response Relationships
Non-Convergence and Overfitting
Some other Goodness of Fit Statistics
Strategy for Model Selection
Predicting a Binary Response Probability
BIOASSAY AND SOME OTHER APPLICATIONS
The Tolerance Distribution
Estimating an Effective Dose
Relative Potency
Natural Response
Non-Linear Logistic Regression Models
Applications of the Complementary Log-Log Model
MODEL CHECKING
Definition of Residuals
Checking the Form of the Linear Predictor
Checking the Adequacy of the Link Function
Identification of Outlying Observations
Identification of Influential Observations
Checking the Assumption of a Binomial Distribution
Model Checking for Binary Data
Summary and Recommendations
OVERDISPERSION
Potential Causes of Overdispersion
Modelling Variability in Response Probabilities
Modelling Correlation Between Binary Responses
Modelling Overdispersed Data
A Model with a Constant Scale Parameter
The Beta-Binomial Model
Discussion
MODELLING DATA FROM EPIDEMIOLOGICAL STUDIES
Basic Designs for Aetiological Studies
Measures of Association Between Disease and Exposure
Confounding and Interaction
The Linear Logistic Model for Data from Cohort Studies
Interpreting the Parameters in a Linear Logistic Model
The Linear Logistic Model for Data from Case-Control Studies
Matched Case-Control Studies
MIXED MODELS FOR BINARY DATA
Fixed and Random Effects
Mixed Models for Binary Data
Multilevel Modelling
Mixed Models for Longitudinal Data Analysis
Mixed Models in Meta-Analysis
Modelling Overdispersion Using Mixed Models
EXACT METHODS
Comparison of Two Proportions Using an Exact Test
Exact Logistic Regression for a Single Parameter
Exact Hypothesis Tests
Exact Confidence Limits for bk
Exact Logistic Regression for a Set of Parameters
Some Examples
Discussion
SOME ADDITIONAL TOPICS
Ordered Categorical Data
Analysis of Proportions and Percentages
Analysis of Rates
Analysis of Binary Time Series
Modelling Errors in the Measurement of Explanatory Variables
Multivariate Binary Data
Analysis of Binary Data from Cross-Over Trials
Experimental Design
COMPUTER SOFTWARE FOR MODELLING BINARY DATA
Statistical Packages for Modelling Binary Data
Interpretation of Computer Output
Using Packages to Perform Some Non-Standard Analyses
Appendix A: Values of logit(p) and probit(p)
Appendix B: Some Derivations
Appendix C: Additional Data Sets
References
Index of Examples
Index

...
View More

Featured Author Profiles

Author - David  Collett
Author

David Collett

Associate Director, Statistics and Clinical Studies, NHS Blood and Transplant
Bristol

Learn more about David Collett »

Reviews

Praise for the first edition:

"…A merit of the book, considerably enhancing its practical value, is the detailed discussion of computational issues and software. … Overall the book provides an accessible and effective presentation of the topic. I recommend it."
-Journal of Applied Statistics

"In summary, this book draws together material on many practical aspects of the analysis of binary data, which was unavailable before in a single book. Applied statisticians, at any level, will learn something from it."
-The Statistician

"…well written, contains good examples, and ideas and concepts are developed and explained logically and clearly…I can strongly recommend this book as a handy reference for applied statisticians and other researchers with a good background in statistical methods… I also appreciated having a book that seems to have very few errors of any kind!"
-Biometrics

Support Material