408 Pages 54 B/W Illustrations
    by Chapman & Hall

    408 Pages
    by Chapman & Hall

    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.

    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

    Biography

    David Collett

    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