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| Ïîèñê ïî óêàçàòåëÿì |
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| Hosmer D.W., Lemeshow S. — Applied logistic regression |
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| Ïðåäìåòíûé óêàçàòåëü |
 tables, generally: classification 159—160 228
tables, generally: contingency 331
tables, generally: fitted logistic regression compared with stratified analysis 79—85
tables, generally: model-building strategies 136—137
 131 164—167 345
1—1 matched study: example of 230—235
1—1 matched study: goodness-of-fit assessment 236—243
1—1 matched study: logistic regression analysis for 226—230
1—M matched study: example of 243—248
1—M matched study: goodness-of-fit assessment 248—259
Additive models 103—104
Adjacent-category logistic model 289 293—295 297—298
Adjustment, statistical 65 67 69
Algorithms, variable selection 96
Analysis of covariance 65
Analysis of variance table 11—12
Asthma study, correlated data analysis 309—312
Asymptotic distribution 132
Asymptotically equivalent 21
Auto-regressive mode, correlated data analysis 313
Baseline logit model 289
Best subsets linear regression, model-building: applications, generatly 128—129
Best subsets linear regression, model-building: interaction 136—137
Best subsets linear regression, model-building: matrix notation 128—131
Best subsets linear regression, model-building: multivariable analysis 132
Best subsets linear regression, model-building: overfitting 134—135
Best subsets linear regression, model-building: sum-of-squares 130—131
Best subsets linear regression, model-building: UIS case illustration 132—135
Best subsets linear regression, model-building: univariate stage 129—130
Best subsets linear regression, model-building: variable selection 96
Best subsets linear regression, model-building: vector notation 128—129
Binary logistic models 1 278 307—308
Binomial distribution 7 116
BMDPLR 121—122
Breslow — Day test 83—84
Calibration, goodness-of-fit assessment 159
Case-control studies: components of 189—190 205—210
Case-control studies: matched See Matched case-control studies
Chi-square distribution: best subsets logistic regression 133
Chi-square distribution: fitted logistic regression 72 82 85
Chi-square distribution: logistic regression 14 16
Chi-square test, variable selection: benefits of 92—93 102
Chi-square test, variable selection: stepwise logistic regression 116
Classification 21
Classification tables, goodness-of-fit assessment 156—160 188
Cluster-level covariates 309
Cluster-specific model, correlated data analysis 310—312 316—317 320—328
Clustering 185—186
Coefficients, generally: correlated See Correlated data analysis
Coefficients, generally: in variable selection 97
Coefficients, generally: testing for significance of 11—17
Cohort studies 203—205
Collinearity 140—141
Complex sample surveys, fitting logistic regression models to data from 211—221
Conditional exact maximum likelihood estimate (CMLE) 334—337
Conditional maximum likelihood estimators 23
Conditional mean 4 6—7 168
Confidence interval estimation: fitted logistic regression 52—53 55 62 88
Confidence interval estimation: logistic regression 17—21
Confidence interval estimation: multiple logistic regression 35 40—42
Confidence interval: estimation See Confidence interval estimation
Confidence interval: in goodness-of-fit assessment 191 195—198
Confidence interval: Wald-based 19—20
Confidence limits 59
Confounding: fitted logistic regression 70—74
Confounding: goodness-of-fit assessment 185
Confounding: variable selection 128
Constrained cumulative logit model 297
Contingency table 92—93
Continuation-ratio logistic model 290 295—298
Continuous covariates: goodness-of-fit 175—176
Continuous covariates: variable selection 93—94 103 126
Continuous independent variable: fitted logistic regression 63—64 67 97
Continuous independent variable: variable selection 97
Continuous variables: defined 22
Continuous variables: independent See Continuous independent variable
Continuous variables: variable selection 93
Coronary heart disease (CHD) study: fitted logistic regression 51—53 56—57 64 71—72
Coronary heart disease (CHD) study: logistic regression 2—7
Correlated data analysis: duster-speci fie 310—312 316—317 320—328
Correlated data analysis: population average model 311—323 329—330
Correlated data analysis: subject-specific 310 312
Correlated data analysis: transitional model 312
Covariance matrix: case-control studies 206—210
Covariance matrix: correlated data analysis 315
Covariance matrix: fitted logistic regression 78
Covariance matrix: logistic regression 19
Covariance matrix: multiple logistic regression 34 41—43
Covariate pattern: defined 144
Covariate pattern: diagnostics and 169 172—183
Covariate pattern: goodness-of-fit assessment 178—179 182 282—287
Covariate pattern: model-building strategies 138
Cox model 205 331—332
Data sets: ICU study 23—25
Data sets: low birth weight study 25
Data sets: prostate cancer study 25—26
Data sets: UMARU IMPACT study 26—28
Degrees-of-freedom: complex sample surveys 214
Degrees-of-freedom: correlated data analysis 328
Degrees-of-freedom: exact computation methods 338
Degrees-of-freedom: fitted linear regression 72 82—83 85
Degrees-of-freedom: goodness-of-fit assessment 154
Degrees-of-freedom: multinomial logistic regression 275
Degrees-of-freedom: multiple logistic regression 38—39
Degrees-of-freedom: ordinal logistic regression model 295 304
Degrees-of-freedom: stepwise logistic regression 116 120
Degrees-of-freedom: variable selection and 92 102 116
Dependent outcome 1
Design matrix 168
Design variables: fitted logistic regression 60—61
Design variables: multiple logistic regression 32 38
Deviance, Pearson 145—147 152—154 179
Deviation from mean coding 59
Deviation from means 54
Diagnostics: basic building blocks 176—178
Diagnostics: coefficient estimation 182
Diagnostics: confounding 185
Diagnostics: correlated data analysis 325—326
Diagnostics: covariate patterns 172—183
Diagnostics: design matrix 168—169
Diagnostics: distribution theory 175
Diagnostics: leverage values 169—172 177
Diagnostics: maximum likelihood estimate (MLE) 184—185
Diagnostics: multinomial logistic regression model 280—287
Diagnostics: Pearson chi-square statistic 174
Diagnostics: residual sum-of-square 167—168
Diagnostics: residuals 178—179
Diagnostics: UIS case study 184
Dichotomous covariate 339—340
Dichotomous independent variable, fitted logistic regression 48—56
Dichotomous logistic regression 1 43
Discrete choice model 260
Discrete variables 22 33
Discriminant analysis 21 138
Discriminant function: analysis See Discriminant function analysis
Discriminant function: multiple logistic regression 43—44
Discrimination, goodness-of-fit assessment 160 163
Distribution functions 5—6
Due regression sum-of-squares 12
Dummy variables 32
Effect modifier 70
EGRET 85 152 326—327
Error: estimated standard 138—139
Error: fitted logistic regression 58
Error: logistic regression 6—7
Error: multiple logistic regression 34 42
Estimation methods: logistic regression models 21—23
Estimation methods: multinomial logistic regression 264—273
Events per parameter 346
Exchangeable correlation matrix 314
| Exchangeable correlation, correlated data analysis 313
Explanatory variables 1
External validation, goodness-of-fit 186—188
Extrabinomial variation 185
F-distribution 213—214
F-test 116
Fisher's Exact Test 334
Fit assessment: 1—1 matched study 236—243
Fit assessment: 1—M matched study 248—259
Fit assessment: exercises 200—202
Fit assessment: external validation 186—118
Fit assessment: logistic regression diagnostics 167—186
Fit assessment: results, interpretation and presentation of 188—200
Fit assessment: summary measures of goodness-of-fit See Goodness-of-fit assessment factors
Fitted logistic regression model, interpretation of: tables, stratified analysis compared with 79—85
Fitted logistic regression model, interpretation of: confounding 70—74
Fitted logistic regression model, interpretation of: continuous independent variable 63—64
Fitted logistic regression model, interpretation of: dichotomous independent variable 48—56
Fitted logistic regression model, interpretation of: exercises 88—90
Fitted logistic regression model, interpretation of: fitted values 85—88
Fitted logistic regression model, interpretation of: interaction 70—74
Fitted logistic regression model, interpretation of: link function 48
Fitted logistic regression model, interpretation of: multivariable model 64—69
Fitted logistic regression model, interpretation of: odds ratio, estimation in presence of interaction 74—79
Fitted logistic regression model, interpretation of: polychotomous independent variable 56—72
Fitted values, fitted logistic regression 85—88
Fitting: assessment of See Fit assessment; Goodness-of-fit assessment
Fitting: correlated data analysis 320 326—330
Fitting: multinomial logistic regression 279—287
Fitting: ordinal logistic regression model 291—292
Fitting: stepwise logistic regression 117—120
Fractional polynomials, variable selection 100—103 109 111
Furnival — Wilson algorithm 128
GEE (generalized estimating equations) 312—316
Generalized additive model, variable selection 103—104
Goodness-of-fit assessment factors: classification tables 156—160 188
Goodness-of-fit assessment factors: Hosmer — Lemeshow tests 147—156 327 338
Goodness-of-fit assessment factors: interaction 188—189
Goodness-of-fit assessment factors: interpretation of 164 183—184
Goodness-of-fit assessment factors: Pearson chi-square statistic and deviance 145—147 152—154 181 218 327
Goodness-of-fit assessment factors: Pearson correlation coefficient 164—165
Goodness-of-fit assessment factors: ROC Curve, area under 160—164
Goodness-of-fit: assessment of See Goodness-of-fit assessment factors
Goodness-of-fit: defined 143
Goodness-of-fit: fitted logistic regression 85
Goodness-of-fit: logistic regression, generally 11 13
Goodness-of-fit: sample size issues 339—347
Grouping, goodness-of-fit 176
Hat matrix 168
Homogeneity test 82—83
Hosmer — Lemeshow tests 147—156 187 327 338
ICU study 23—25
Identity function 47
Independent model, correlated data analysis 313
Independent variables: continuous 63—69
Independent variables: dichotomous 48—56
Independent variables: fitted logistic regression 47 54
Independent variables: logistic regression 1—2 6—7 14
Independent variables: polychotomous 56—62
Information sandwich estimator 315
Interaction: best subsets linear regression 136—137
Interaction: defined 69
Interaction: fitted logistic regression 70—74 98
Interaction: goodness-of-fit assessment 188—189
Interaction: stepwise logistic regression 125—128
Interaction: variable selection 98—99
Intercept coefficient 47 135
Least squares: logistic regression 7—8
Least squares: multiple logistic regression 43
Leverage values, goodness-of-fit assessment 169—172 177
Likelihood equations 8 33
Likelihood function: logistic regression model, generally 8
Likelihood function: multinomial logic regression 262
Likelihood function: multiple logistic regression 33
Likelihood function: sampling models 206 209 212
Likelihood ratio test: correlated data analysis 321
Likelihood ratio test: goodness-of-fit assessment 146
Likelihood ratio test: logistic regression 13 15—16
Likelihood ratio test: multiple logistic regression 37
Likelihood ratio test: variable selection 97 120
Likelihood ratio: defined 13
Likelihood ratio: multinomial logistic regression 276—277
Likelihood ratio: ordinal logistic regression model 305—307
Likelihood ratio: stepwise linear regression 117
Likelihood ratio: test See Likelihood ratio test
Likelihood ratio: variable selection and 92 117
Limits, fitted logistic regression 59
Linear regression model, logistic distinguished from with 1 6—7
Link function, fitted logistic regression 47—48
Log likelihood function: best subsets linear regression 123
Log likelihood function: case-control studies 211
Log likelihood function: correlated data analysis 316
Log likelihood function: logistic regression model, generally 8 15
Log likelihood function: multinomial logistic regression 270
Log likelihood function: multiple logistic regression 34
Log-odds ratio: fitted linear regression 63—64 67—68 75—76 79
Log-odds ratio: linear regression 18
Logistic distribution, generally 6
Logistic regression model: coefficients, testing for significance of 11—17
Logistic regression model: confidence interval estimation 17—23
Logistic regression model: data sets 23—28
Logistic regression model: estimation methods 21—23
Logistic regression model: exact methods for 330—339
Logistic regression model: example of 2—7
Logistic regression model: exercises 29—30
Logistic regression model: fitting 7—10
Logistic regression model: interpretation of See Fitted logistic regression model
Logistic regression model: model-building strategies See Model-building strategies
Logistic regression model: purpose of 1
Logistic regression model: sample size issues 339—347
Logit: fitted logistic regression 48 67 70—71 73—75
Logit: logistic regression 17—19
Logit: multiple logistic regression 31—32 40 42
Logit: transformation 6 48
Logit: variable selection 97
LogXact 4 331 334 338
Low birth weight study: correlated data analysis 318—330
Low birth weight study: fitted linear regression 77 80 86—88
Low birth weight study: logistic regression 25
Low birth weight study: matched case-control study 230—243
Low birth weight study: multiple logistic regression 35—38
Low birth weight study: ordinal logistic regression model 292—308
m-asymptotics 145 147 150 175—176 187 210
Main effects model 98
Mammography experience study 265—287
Mantel — Haenszel estimator 79—80 82—83 85
Matched case-control studies 1—M matched study
Matched case-control studies, characteristics of 223—226
Matrix notation, best subsets logistic regression 128—129. See also Specific types of matrices
Maximum likelihood estimation (MLE): best subsets linear regression 128 130—131
Maximum likelihood estimation (MLE): case-control studies 208 226
Maximum likelihood estimation (MLE): fitted logistic regression 63 84
Maximum likelihood estimation (MLE): goodness-of-fit assessment 173 184
Maximum likelihood estimation (MLE): logistic regression 8—10 23
Maximum likelihood estimation (MLE): matched case-control studies 226—227
Maximum likelihood estimation (MLE): multinomial logistic regression 263
Maximum likelihood estimation (MLE): multiple logistic regression 33
Maximum likelihood estimation (MLE): ordinal logistic regression models 291
Maximum likelihood estimation (MLE): stepwise logistic regression 121
Maximum likelihood estimation (MLE): variable selection 100
Median unbiased estimator (MUE) 337
Model-building strategies: best subsets 128—135
Model-building strategies: covariates 138
Model-building strategies: exercises 142
Model-building strategies: fitting 140
Model-building strategies: numerical problems 135—141
Model-building strategies: overfitting 135—136
Model-building strategies: pooling 136—137
Model-building strategies: stepwise logistic regression 116—128
Model-building strategies: variable selection 92—116
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