The new edition of this important text has been completely revised and expanded to become the most up-to-date and thorough professional reference text in this fast-moving and important area of biostatistics. Two new chapters have been added on fully parametric models for discrete repeated measures data and on statistical models for time-dependent predictors where there may be feedback between the predictor and response variables. It also contains the many useful features of the previous edition such as, design issues, exploratory methods of analysis, linear models for continuous data, and models and methods for handling data and missing values.
Author(s): Peter Diggle, Patrick Heagerty, Kung-Yee Liang, Scott Zeger
Edition: 2
Year: 2002
Language: English
Pages: 396
ANALYSIS OF LONGITUDINAL DATA, 2ND ED.......Page 1
Oxford Statistical Science Series......Page 2
Title Page......Page 3
Copyright Page......Page 4
Dedication......Page 5
Preface......Page 7
Contents......Page 11
1.1 Longitudinal studies......Page 17
1.2 Examples......Page 19
1.3 Notation......Page 31
1.4 Merits of longitudinal studies......Page 32
1.5 Approaches to longitudinal studies......Page 33
1.6 Organization of subsequent chapters......Page 36
2.2 Bias......Page 38
2.3 Efficiency......Page 40
2.4 Sample size calculations......Page 42
2.5 Further reading......Page 47
3.1 Introduction......Page 49
3.2 Graphical presentation of longitudinal data......Page 50
3.3 Fitting smooth curves to longitudinal data......Page 57
3.4 Exploring correlation structure......Page 62
3.5 Exploring association amongst categorical responses......Page 68
3.6 Further reading......Page 69
4.1 Motivation......Page 70
4.2.1 The uniform correlation model......Page 71
4.2.2 The exponential correlation model......Page 72
4.2.3 Two-stage least-squares estimation and random effects models......Page 73
4.3 Weighted least-squares estimation......Page 75
4.4 Maximum likelihood estimation under Gaussian assumptions......Page 80
4.5 Restricted maximum likelihood estimation......Page 82
4.6 Robust estimation of standard errors......Page 86
5.1 Introduction......Page 97
5.2 Models......Page 98
5.2.1 Pure serial correlation......Page 100
5.2.2 Serial correlation plus measurement error......Page 105
5.2.3
Random intercept plus serial correlation plus measurement error......Page 106
5.2.4 Random effects plus measurement error......Page 107
5.3 Model-fitting......Page 109
5.3.1 Formulation......Page 110
5.3.2 Estimation......Page 111
5.3.3 Inference......Page 113
5.3.4 Diagnostics......Page 114
5.4 Examples......Page 115
5.5 Estimation of individual trajectories......Page 126
5.6 Further reading......Page 129
6.1 Preliminaries......Page 130
6.2 Time-by-time ANOVA......Page 131
6.3 Derived variables......Page 132
6.4 Repeated measures......Page 139
6.5 Conclusions......Page 141
7.1 Marginal models......Page 142
7.2 Random effects models......Page 144
7.3 Transition (Markov) models......Page 146
7.4 Contrasting approaches......Page 147
7.5 Inferences......Page 153
8.1 Introduction......Page 157
8.2.1 The log-linear model......Page 158
8.2.2 Log-linear models for marginal means......Page 159
8.2.3 Generalized estimating equations......Page 162
8.3 Examples......Page 164
8.4.1 Parametric modelling for count data......Page 176
8.4.2 Generalized estimating equation approach......Page 178
8.5 Sample size calculations revisited......Page 181
8.6 Further reading......Page 183
9.1 Introduction......Page 185
9.2.1 Conditional likelihood......Page 187
9.2.2 Maximum likelihood estimation......Page 188
9.3.1 Conditional likelihood approach......Page 191
9.3.2 Random effects models for binary data......Page 194
9.3.3 Examples of logistic models with Gaussian random effects......Page 196
9.4.1 Conditional likelihood method......Page 200
9.4.2 Random effects models for counts......Page 202
9.4.3 Poisson-Gaussian random effects models......Page 204
9.5 Further reading......Page 205
10.1 General......Page 206
10.2 Fitting transition models......Page 208
10.3 Transition models for categorical data......Page 210
10.3.1 Indonesian children's study example......Page 213
10.3.2 Ordered categorical data......Page 217
10.4 Log-linear transition models for count data......Page 220
10.5 Further reading......Page 222
11.1 Introduction......Page 224
11.2 Generalized linear mixed models......Page 225
11.2.1 Maximum likelihood algorithms......Page 228
11.2.2 Bayesian methods......Page 230
11.3 Marginalized models......Page 232
11.3.1 An example using the Gaussian linear model......Page 234
11.3.2 Marginalized log-linear models......Page 236
11.3.3 Marginalized latent variable models......Page 238
11.3.4 Marginalized transition models......Page 241
11.4.1 Crossover data......Page 247
11.4.2 Madras schizophrenia data......Page 250
11.5 Summary and further reading......Page 259
12.1 Introduction......Page 261
12.2 An example: the MSCM study......Page 263
12.3 Stochastic covariates: full and partly conditional means......Page 269
12.3.1 Estimation issues with cross-sectional models......Page 270
12.3.2 A simulation illustration......Page 272
12.3.3 MSCM data and cross-sectional analysis......Page 273
12.3.4 Summary......Page 274
12.4.1 A single lagged covariate......Page 275
12.4.2 Multiple lagged covariates......Page 276
12.4.3 MSCM data and lagged covanates......Page 277
12.5 Time-dependent confounders......Page 281
12.5.1 Feedback: response is an intermediate and a confounder......Page 282
12.5.2 MSCM data and endogeneity......Page 284
12.5.3 Targets of inference......Page 285
12.5.4 Estimation using g-computation......Page 289
12.5.5 MSCM data and g-computation......Page 291
12.5.6 Estimation using inverse probability of treatment weights (IPTW)......Page 292
12.5.7 MSCM data and marginal structural models using IPTW......Page 295
12.6 Summary and further reading......Page 296
13.1 Introduction......Page 298
13.2 Classification of missing value mechanisms......Page 299
13.3 Intermittent missing values and dropouts......Page 300
13.4.1 Last observation carried forward......Page 303
13.5 Testing for completely random dropouts......Page 304
13.6 Generalized estimating equations under a random missingness mechanism......Page 309
13.7.1 Selection models......Page 311
13.7.2 Pattern mixture models......Page 315
13.7.3 Random effect models......Page 317
13.7.4 Contrasting assumptions: a graphical representation......Page 319
13.8 A longitudinal trial of drug therapies for schizophrenia......Page 321
13.9 Discussion......Page 332
14.1 Non-parametric modelling of the mean response......Page 335
14.2 Non-linear regression modelling......Page 342
14.2.1 Correlated errors......Page 344
14.3 Joint modelling of longitudinal measurements and recurrent events......Page 345
14.4 Multivariate longitudinal data......Page 348
A.2 The linear model and the method of least squares......Page 353
A.3 Multivariate Gaussian theory......Page 355
A.4 Likelihood inference......Page 356
A.5.1 Logistic regression......Page 359
A.5.2 Poisson regression......Page 360
A.5.3 The general class......Page 361
A.6 Quasi-likelihood......Page 362
Bibliography......Page 365
Index......Page 385
Back Cover......Page 396
