Modelling binary data / David Collett.

Format:

• Book

Author/Creator:

• Collett, D., 1952-

Series:

• Texts in statistical science
• Chapman & Hall/CRC texts in statistical science series

Language:

English

Subjects (All):

• Analysis of variance.
• Distribution (Probability theory).
• Linear models (Statistics).
• Analysis of Variance.
• Linear Models.

Medical Subjects:

• Analysis of Variance.
• Linear Models.

Physical Description:

387 pages : illustrations ; 24 cm.

Edition:

Second edition.

Place of Publication:

Boca Raton : Chapman & Hall/CRC, [2003]

Contents:

• 1.1 Some examples 1
• 1.2 The scope of this book 14
• 1.3 Use of statistical software 15
• 2 Statistical inference for binary data 19
• 2.1 The binomial distribution 19
• 2.2 Inference about the success probability 23
• 2.3 Comparison of two proportions 31
• 2.4 Comparison of two or more proportions 38
• 3 Models for binary and binomial data 45
• 3.1 Statistical modelling 45
• 3.2 Linear models 47
• 3.3 Methods of estimation 50
• 3.4 Fitting linear models to binomial data 53
• 3.5 Models for binomial response data 56
• 3.6 The linear logistic model 58
• 3.7 Fitting the linear logistic model to binomial data 59
• 3.8 Goodness of fit of a linear logistic model 65
• 3.9 Comparing linear logistic models 71
• 3.10 Linear trend in proportions 78
• 3.11 Comparing stimulus-response relationships 81
• 3.12 Non-convergence and overfitting 85
• 3.13 Some other goodness of fit statistics 87
• 3.14 Strategy for model selection 91
• 3.15 Predicting a binary response probability 98
• 4 Bioassay and some other applications 103
• 4.1 The tolerance distribution 103
• 4.2 Estimating an effective dose 106
• 4.3 Relative potency 111
• 4.4 Natural response 114
• 4.5 Non-linear logistic regression models 118
• 4.6 Applications of the complementary log-log model 122
• 5 Model checking 129
• 5.1 Definition of residuals 130
• 5.2 Checking the form of the linear predictor 135
• 5.3 Checking the adequacy of the link function 146
• 5.4 Identification of outlying observations 150
• 5.5 Identification of influential observations 154
• 5.6 Checking the assumption of a binomial distribution 168
• 5.7 Model checking for binary data 169
• 6 Overdispersion 195
• 6.1 Potential causes of overdispersion 195
• 6.2 Modelling variability in response probabilities 199
• 6.3 Modelling correlation between binary responses 201
• 6.4 Modelling overdispersed data 202
• 6.5 A model with a constant scale parameter 206
• 6.6 The beta-binomial model 211
• 7 Modelling data from epidemiological studies 215
• 7.1 Basic designs for aetiological studies 216
• 7.2 Measures of association between disease and exposure 219
• 7.3 Confounding and interaction 223
• 7.4 The linear logistic model for data from cohort studies 226
• 7.5 Interpreting the parameters in a linear logistic model 230
• 7.6 The linear logistic model for data from case-control studies 242
• 7.7 Matched case-control studies 250
• 8 Mixed models for binary data 269
• 8.1 Fixed and random effects 269
• 8.2 Mixed models for binary data 270
• 8.3 Multilevel modelling 277
• 8.4 Mixed models for longitudinal data analysis 284
• 8.5 Mixed models in meta-analysis 291
• 8.6 Modelling overdispersion using mixed models 293
• 9 Exact Methods 303
• 9.1 Comparison of two proportions using an exact test 303
• 9.2 Exact logistic regression for a single parameter 307
• 9.3 Exact hypothesis tests 312
• 9.4 Exact confidence limits for [beta subscript kappa] 317
• 9.5 Exact logistic regression for a set of parameters 318
• 9.6 Some examples 319
• 10 Some additional topics 325
• 10.1 Ordered categorical data 325
• 10.2 Analysis of proportions and percentages 329
• 10.3 Analysis of rates 330
• 10.4 Analysis of binary time series 331
• 10.5 Modelling errors in the measurement of explanatory variables 331
• 10.6 Multivariate binary data 332
• 10.7 Analysis of binary data from cross-over trials 333
• 10.8 Experimental design 333
• 11 Computer software for modelling binary data 335
• 11.1 Statistical packages for modelling binary data 335
• 11.2 Interpretation of computer output 339
• 11.3 Using packages to perform some non-standard analyses 341
• Appendix A Values of logit(p) and probit(p) 351
• Appendix B Some derivations 353
• B.1 An algorithm for fitting a GLM to binomial data 353
• B.2 The likelihood function for a matched case-control study 357
• Appendix C Additional data sets 361
• C.1 Tonsil size 361
• C.2 Toxicity of rotenone 361
• C.3 Food poisoning 362
• C.4 Analgesic potency of four compounds 362
• C.5 Vasoconstriction of the fingers 363
• C.6 Treatment of neuralgia 363
• C.7 HIV infection 365
• C.8 Aplastic anaemia 365
• C.9 Cancer of the cervix 367
• C.10 Endometrial cancer 367.

Notes:

Includes bibliographical references (pages [369]-378) and indexes.

ISBN:

• 1584883243
• 9781584883241

OCLC:

50065016

Publisher Number:

99942974071

Online:

Publisher description