Analysis of longitudinal data:
Gespeichert in:
| Format: | Buch |
|---|---|
| Sprache: | English |
| Veröffentlicht: |
Oxford
Oxford Univ. Press
2009
|
| Ausgabe: | 2. ed., repr. |
| Schriftenreihe: | Oxford statistical science series
25 |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | XV, 379 S. graph. Darst. |
| ISBN: | 9780198524847 |
Internformat
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| 245 | 1 | 0 | |a Analysis of longitudinal data |c Peter J. Diggle ... |
| 250 | |a 2. ed., repr. | ||
| 264 | 1 | |a Oxford |b Oxford Univ. Press |c 2009 | |
| 300 | |a XV, 379 S. |b graph. Darst. | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
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| 490 | 1 | |a Oxford statistical science series |v 25 | |
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| 650 | 0 | 7 | |a Zeitreihenanalyse |0 (DE-588)4067486-1 |2 gnd |9 rswk-swf |
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Datensatz im Suchindex
| _version_ | 1804138683122581504 |
|---|---|
| adam_text | Contents
1
Introduction
1
1.1
Longitudinal
studies
1
1.2
Examples
З
1.3
Notation
15
1.4
Merits of longitudinal studies
16
1.5
Approaches to longitudinal data analysis
17
1.6
Organization of subsequent chapters
20
2
Design considerations
22
2.1
Introduction
22
2.2
Bias
22
2.3
Efficiency
24
2.4
Sample size calculations
26
2.4.1
Continuous responses
28
2.4.2
Binary responses
30
2.5
Further reading
31
3
Exploring longitudinal data
33
3.1
Introduction
33
3.2
Graphical presentation of longitudinal data
34
3.3
Fitting smooth curves to longitudinal data
41
3.4
Exploring correlation structure
46
3.5
Exploring association amongst categorical responses
52
3.6
Further reading
53
4
General linear models for longitudinal data
54
4.1
Motivation
54
4.2
The general linear model with correlated errors
55
4.2.1
The uniform correlation model
55
4.2.2
The exponential correlation model
56
4.2.3
Two-stage least-squares estimation and random
effects models
57
4.3
Weighted least-squares estimation
59
4.4
Maximum likelihood estimation under Gaussian assumptions
64
4.5
Restricted maximum likelihood estimation
66
4.6
Robust estimation of standard errors
70
CONTENTS
Parametric models for covariance structure
81
5.1
Introduction
81
5.2
Models
82
5.2.1
Pure serial correlation
84
5.2.2
Serial correlation plus measurement error
89
5.2.3
Random intercept plus serial correlation plus
measurement error
90
5.2.4
Random effects plus measurement error
91
5.3
Model-fitting
93
5.3.1
Formulation
94
5.3.2
Estimation
95
5.3.3
Inference
97
5.3.4
Diagnostics
98
5.4
Examples
99
5.5
Estimation of individual trajectories
110
5.6
Further reading
113
Analysis of variance methods
114
6.1
Preliminaries
114
6.2
Time-by-time ANOVA
115
6.3
Derived variables
116
6.4
Repeated measures
123
6.5
Conclusions
125
Generalized linear models for longitudinal data
126
7.1
Marginal models
126
7.2
Random effects models
128
7.3
Transition (Markov) models
130
7.4
Contrasting approaches
131
7.5
Inferences
137
Marginal models
141
8.1
Introduction
141
8.2
Binary responses
142
8.2.1
The log-linear model
142
8.2.2
Log-linear models for marginal means
143
8.2.3
Generalized estimating equations
146
8.3
Examples
148
8.4
Counted responses
160
8.4.1
Parametric modelling for count data
160
8.4.2
Generalized estimating equation approach
162
8.5
Sample size calculations revisited
165
8.6
Further reading
167
CONTENTS xiii
9
Random effects models
169
9.1
Introduction
169
9.2
Estimation for generalized linear mixed models
171
9.2.1
Conditional likelihood
171
9.2.2
Maximum likelihood estimation
172
9.3
Logistic regression for binary responses
175
9.3.1
Conditional likelihood approach
175
9.3.2
Random effects models for binary data
178
9.3.3
Examples of logistic models with Gaussian
random effects
180
9.4
Counted responses
184
9.4.1
Conditional likelihood method
184
9.4.2
Random effects models for counts
186
9.4.3
Poisson-Gaussian random effects models
188
9.5
Further reading
189
10
Transition models
190
10.1
General
190
10.2
Fitting transition models
192
10.3
Transition models for categorical data
194
10.3.1
Indonesian children s study example
197
10.3.2
Ordered categorical data
201
10.4
Log-linear transition models for count data
204
10.5
Further reading
206
11
Likelihood-based methods for categorical data
208
11.1
Introduction
208
11.1.1
Notation and definitions
209
11.2
Generalized linear mixed models
209
11.2.1
Maximum likelihood algorithms
212
11.2.2
Bayesian methods
214
11.3
Marginalized models
216
11.3.1
An example using the Gaussian linear model
218
11.3.2
Marginalized log-linear models
220
11.3.3
Marginalized latent variable models
222
11.3.4
Marginalized transition models
225
11.3.5
Summary
231
11.4
Examples
231
11.4.1
Crossover data
231
11.4.2
Madras schizophrenia data
234
11.5
Summary and further reading
243
12
Time-dependent covariates
245
12.1
Introduction
245
12.2
An example: the MSCM study
247
xiv CONTENTS
12.3
Stochastic covariates
253
12.3.1
Estimation issues with cross-sectional models
254
12.3.2
A simulation illustration
256
12.3.3
MSCM data and cross-sectional analysis
257
12.3.4
Summary
258
12.4
Lagged covariates
259
12.4.1
A single lagged covariate
259
12.4.2
Multiple lagged covariates
260
12.4.3
MSCM data and lagged covariates
261
12.4.4
Summary
265
12.5
Time-dependent confounders
265
12.5.1
Feedback: response is an intermediate and a
confounder
266
12.5.2
MSCM data and endogeneity
268
12.5.3
Targets of inference
269
12.5.4
Estimation using ^-computation
273
12.5.5
MSCM data and ^-computation
275
12.5.6
Estimation using inverse probability of treatment
weights (IPTW)
276
12.5.7
MSCM data and marginal structural models
using IPTW
279
12.5.8
Summary
280
12.6
Summary and further reading
280
13
Missing values in longitudinal data
282
13.1
Introduction
282
13.2
Classification of missing value mechanisms
283
13.3
Intermittent missing values and dropouts
284
13.4
Simple solutions and their limitations
287
13.4.1
Last observation carried forward
287
13.4.2
Complete case analysis
288
13.5
Testing for completely random dropouts
288
13.6
Generalized estimating equations under a random
missingness mechanism
293
13.7
Modelling the dropout process
295
13.7.1
Selection models
295
13.7.2
Pattern mixture models
299
13.7.3
Random effect models
301
13.7.4
Contrasting assumptions: a graphical
representation
303
13.8
A longitudinal trial of drug therapies for schizophrenia
305
13.9
Discussion
316
CONTENTS xv
14
Additional topics
319
14.1
Non-parametric modelling of the mean response
319
14.1.1
Further reading
326
14.2
Non-linear regression modelling
326
14.2.1
Correlated errors
328
14.2.2
Non-linear random effects
329
14.3
Joint modelling of longitudinal measurements
and recurrent events
329
14.4
Multivariate longitudinal data
332
Appendix Statistical background
337
A.I Introduction
337
A.2 The linear model and the method of least squares
337
A.3 Multivariate Gaussian theory
339
A.4 Likelihood inference
340
A.
5
Generalized linear models
343
A.5.1 Logistic regression
343
A.5.2
Poisson
regression
344
A.5.3 The general class
345
A.
6
Quasi-likelihood
346
Bibliography
349
Index
369
|
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| discipline | Mathematik Wirtschaftswissenschaften |
| edition | 2. ed., repr. |
| format | Book |
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| id | DE-604.BV035358900 |
| illustrated | Illustrated |
| indexdate | 2024-07-09T21:32:05Z |
| institution | BVB |
| isbn | 9780198524847 |
| language | English |
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| physical | XV, 379 S. graph. Darst. |
| publishDate | 2009 |
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| publisher | Oxford Univ. Press |
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| series2 | Oxford statistical science series |
| spelling | Analysis of longitudinal data Peter J. Diggle ... 2. ed., repr. Oxford Oxford Univ. Press 2009 XV, 379 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Oxford statistical science series 25 Längsschnittuntersuchung Multivariate Analyse (DE-588)4040708-1 gnd rswk-swf Zeitreihenanalyse (DE-588)4067486-1 gnd rswk-swf Multivariate Analyse (DE-588)4040708-1 s Zeitreihenanalyse (DE-588)4067486-1 s 1\p DE-604 DE-604 Diggle, Peter 1950- Sonstige (DE-588)134228014 oth Oxford statistical science series 25 (DE-604)BV001908661 25 Digitalisierung UB Bamberg application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=017162927&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Analysis of longitudinal data Oxford statistical science series Längsschnittuntersuchung Multivariate Analyse (DE-588)4040708-1 gnd Zeitreihenanalyse (DE-588)4067486-1 gnd |
| subject_GND | (DE-588)4040708-1 (DE-588)4067486-1 |
| title | Analysis of longitudinal data |
| title_auth | Analysis of longitudinal data |
| title_exact_search | Analysis of longitudinal data |
| title_full | Analysis of longitudinal data Peter J. Diggle ... |
| title_fullStr | Analysis of longitudinal data Peter J. Diggle ... |
| title_full_unstemmed | Analysis of longitudinal data Peter J. Diggle ... |
| title_short | Analysis of longitudinal data |
| title_sort | analysis of longitudinal data |
| topic | Längsschnittuntersuchung Multivariate Analyse (DE-588)4040708-1 gnd Zeitreihenanalyse (DE-588)4067486-1 gnd |
| topic_facet | Längsschnittuntersuchung Multivariate Analyse Zeitreihenanalyse |
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