Modelling Binary Data (Hardback)

Part 1 Introduction: some examples; the scope of this book; use of statistical software. Part 2 Statistical inference for binary data: the binomial distribution; inference about the success probability; comparison of two proportions; comparison of two or more proportions. Part 3 Models for binary and binomial data: statistical modelling; linear models; methods of estimation; fitting linear models to binary data; models for binary response data; the linear logistic model; fitting the linear logistic model to binomial data; goodness of fit of a logistic model; comparing linear logistic models; linear trends in proportions; comparing stimulus-response relationships; non-convergence and over-fitting; a further example on model selection; predicting a binary response probability. Part 4 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. Part 5 Definition of residuals; checking the form of the linear predictor; checking the adequacy of the link function; identification of outlying observations; indentification of influential observations; checking the assumption of a binomial distribution; model checking for binary data; a further example on the use of diagnostics. Part 6 Overdispersion: potential causes of overdispersion; modelling variability in response probabilities; modelling correlation between binary responses; modelling overdispersed data; the special case of equal n2; the beta-binomial model; random effects in a linear logistic model; summary and recommendations; a further example. Part 7 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; a matched case-control studies; a matched case-control study on sudden infant death syndrome. Part 8 Some additional topics: analysis of proportions and percentages; analysis of rates; analysis of binary data from cross-over trials; random effects modelling; modelling errors in the measurement of explanatory variables; analysis of binary time series; multivariate binary data; experimental design. Part 9 Computer software for modelling binary data: statistical packages for modelling binary data; computer-based analyses of example data sets; using packages to perform some non-standard analyses; summary of the relative merits of packages for modelling binary data. Appendices: Tables of the logistic and probit transformations; algorithm for fitting the linear logistic model; glim macros used in modelling binary data.