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| Ïîèñê ïî óêàçàòåëÿì |
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| Verbeke G., Molenberghs G. — Linear Mixed Models for Longitudinal Data |
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| Ïðåäìåòíûé óêàçàòåëü |
Random effects, histogram 79 82
Random effects, homogeneity model see “Homogeneity model”
Random effects, marginal testing 69 73 133 408 474
Random effects, mixture distribution 85 90 169 172
Random effects, model building 125—128 133
Random effects, normal quantile plot 79 89
Random effects, normality assumption 79 83—92 169 170
Random effects, preliminary 125—128 139 474
Random effects, random intercept 81 117 250 252 253 262 498 504
Random effects, random slope 252 262
Random effects, RANDOM statement 97
Random effects, scatter plot 79 82
Random effects, semi-variogram 144 148
Random effects, shrinkage 80 82 84 85
Random effects, t-test 79
Random effects, versus fixed effects 198—200
Random effects, versus serial correlation 149
RANDOM statement 97 117—119 259 260 267 446 488 500 501
RANDOM statement, versus REPEATED statement 117 119
RANDOM statement, “gcorr” option 98
RANDOM statement, “group=” option 103
RANDOM statement, “g” option 98 253
RANDOM statement, “nofullz” option 488
RANDOM statement, “solution” option 98
RANDOM statement, “subject=” option 97
RANDOM statement, “type=” option 98 104 118
RANDOM statement, “v=” option 98
RANDOM statement, “vcorr=” option 98
RANDOM statement, “vcorr” option 98 101
RANDOM statement, “v” option 98 101
Random-coefficient-based model see “Missing data”
Random-intercepts model 25 68 117 118 120
Random-intercepts model, compound symmetry see “Compound symmetry”
Random-intercepts model, empirical Bayes estimation (EB) 81
Random-intercepts model, semi-variogram 142 144
Random-intercepts model, shrinkage 81
Rat data 7—9
Rat data, efficiency 394
Rat data, inference fixed effects 67
Rat data, inference variance components 66—68
Rat data, information criteria 75
Rat data, linear mixed model 25
Rat data, local influence 307—312
Rat data, marginal versus hierarchical 52 67
Rat data, model misspecification 52
Rat data, power 393 394
Rat data, power distribution 397—104
Rat data, sensitivity analysis 307—312
Rat data, two-stage analysis 21 38
Rat data, variance function 53
REPEATED statement 98 117 119 251 252 259 261 267 446 488 500 501
REPEATED statement, versus RANDOM statement 117—119
REPEATED statement, “group=” option 103 245 359
REPEATED statement, “local=” option 483
REPEATED statement, “local” option 104 130
REPEATED statement, “r=” option 101 245
REPEATED statement, “rcorr=” option 101 245
REPEATED statement, “rcorr” option 101 243
REPEATED statement, “r” option 101 243
REPEATED statement, “subject=” option 100 446
REPEATED statement, “type=AR(l)” option 252
REPEATED statement, “type=” option 100 104 118 119 129 139 256 483 488
Residual covariance structure see “Covariance structure”
Residuals 151 240
Residuals, covariance structure 160 163
Residuals, marginal 151
Residuals, mean structure 160 163
Residuals, ordinary least squares 32 34 53 125 136 139
Residuals, random effects 77 152
Residuals, subject-specific 145 151
Restricted maximum likelihood estimation (REML) 43—47 195
Restricted maximum likelihood estimation (REML), comparison with ML 46—48 139 199
Restricted maximum likelihood estimation (REML), error contrasts 43—46 63 75
Restricted maximum likelihood estimation (REML), fixed effects 45
Restricted maximum likelihood estimation (REML), justification 46 195
Restricted maximum likelihood estimation (REML), likelihood function 46
Restricted maximum likelihood estimation (REML), linear mixed model 44
Restricted maximum likelihood estimation (REML), linear regression 43 48
Restricted maximum likelihood estimation (REML), normal population 43 48
Restricted maximum likelihood estimation (REML), variance components 45
Ridge regression 146
Robust inference see “Fixed effects”
Sample-size calculations see “Design considerations”
Sampling framework, naive 377 380 382
Sampling framework, unconditional 377 382
Sandwich estimator see “Fixed effects”
SAS data set 95
SAS macro 38 162 195 240 332 352 353 359 361 374
Satterthwaite method see “Degrees of freedom”
Saturated mean structure see “Mean structure”
Schwarz information criterion (SBC) see “Information criteria”
selection model 216 231—273 278—279 295—330 333 448 454
Selection model, Heckman's model 296
Semi-variogram 141—148 270 271 419
| Semi-variogram, random effects 144—148
Semi-variogram, random intercepts 142—144 449 452 473
Sensitivity 236—238 270 297
Sensitivity analysis 213 270 277—279 292 448—470
Sensitivity analysis, pattern-mixture model 331—374
Sensitivity analysis, selection model 295 330
Separability condition see “Missing data”
Serial correlation 26—28 128—132 135—150
Serial correlation, check for 136—137
Serial correlation, exponential 28 100 139 142 474
Serial correlation, flexible models 137—140
Serial correlation, fractional polynomials 137—139
Serial correlation, Gaussian 28 100 129 139 142 416 474
Serial correlation, versus random effects 149
Shapiro — Wilk test 136 179 181 186
Shrinkage see “Bayesian methods”
Simplex algorithm 232 235 240
SPlus 493—513
SPlus, comparison with MLwiN 497
SPlus, LME function 493—497
SPlus, LME function, “cluster” argument 494
SPlus, LME function, “covariate.transformation” argument 494
SPlus, LME function, “est.method” argument 495
SPlus, LME function, “fixed” argument 494
SPlus, LME function, “random” argument 494
SPlus, LME function, “re.block” argument 494
SPlus, LME function, “re.paramtr” argument 494
SPlus, LME function, “serial” argument 494
SPlus, LME function, “var.covariate” argument 494
SPlus, LME function, “var.estimate” argument 494
SPlus, LME function, “var.function” argument 494
SPlus, LME.FORMULA function see “SPlus LME
SPlus, NMLE function 493
SPlus, OSWALD see “OSWALD”
Starting values 131
Stationarity see “Covariance structure”
Stratification, posthoc 366
Subject-specific profiles, alignment 448—451
Subject-specific profiles, coefficient of multiple determination 35—38 40
Subject-specific profiles, exploration 35—10 205 288 307
Subject-specific profiles, F test 37—10
Subject-specific profiles, goodness-of-fit 35—37
Summary statistics 23
Surrogate endpoints 420—446
Sweep operator 389
t-distribution 314 318
t-distribution, degrees of freedom 318
t-test, degrees of freedom 57 112
t-test, fixed effects see “Fixed effects”
t-test, random effects see “Random effects”
Time series 28
Time-independent covariate 125 194
Time-varying covariate 95 120 125 190
Tobit model 231—232
Toenail data 9 10 227—229 233—238
Toenail data, MAR analysis 233—234
Toenail data, MCAR analysis 227 229
Toenail data, MNAR analysis 234—238
Toenail data, pattern-mixture model 281—287
toeplitz see “Covariance structure”
Two-stage analysis 20—23 123 133 231 429—430
Two-stage analysis, stage 1 20 35—40 429
Two-stage analysis, stage 2 20 430
Uncertainty, modeling 336
Uncertainty, sampling 336
Unstructured covariance see “Covariance structure”
Untestable assumptions 236 270 281 297 329 334 342 498
Variance components 41 407 415
Variance components, estimation problems 50—52
Variance components, inference 64 73 133
Variance components, local influence see “Local influence”
Variance components, LR test 65 69—73 106 392 408 474
Variance components, maximum likelihood 42
Variance components, negative 54 68
Variance components, restricted maximum likelihood 45
Variance components, Wald test 64 107
VARIANCE function see “Covariance structure”
Variogram see “Semi-variogram”
Vorozole study 15 201—207 270—273
Vorozole study, correlation structure 34
Vorozole study, mean structure 32
Vorozole study, pattern-mixture model 287—291
Vorozole study, pattern-mixture model, sensitivity analysis 352—373
Vorozole study, selection model 270 273
Vorozole study, semi-variogram 144
Vorozole study, variance function 33
Wald test 382
Wald test, fixed effects see “Fixed effects”
Wald test, pattern-mixture model 286
Wald test, scaled 57
Wald test, variance components see “Variance components”
WHERE statement 119
Wilk's Lambda test 119
Within-imputation variance 337
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