Applied longitudinal analysis:
Gespeichert in:
| Beteiligte Personen: | , , |
|---|---|
| Format: | Buch |
| Sprache: | Englisch |
| Veröffentlicht: |
Hoboken, NJ
Wiley
2011
|
| Ausgabe: | 2. ed. |
| Schriftenreihe: | Wiley series in probability and statistics
|
| Schlagwörter: | |
| Links: | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=024410453&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| Beschreibung: | "Since the publication of the first edition, the authors have solicited feedback from both the instructors who use the book as a text for their courses as well as the researchers who use the book as a resource for their research. Thus, the improved Second Edition of Applied Longitudinal Analysis features many additions and revisions based on the feedback of readers, making it the go-to reference for applied use in public health, epidemiology, and pharmaceutical sciences"-- Provided by publisher. |
| Umfang: | XXV, 701 S. graph. Darst. |
| ISBN: | 9780470380277 |
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| 245 | 1 | 0 | |a Applied longitudinal analysis |c Garrett M. Fitzmaurice ; Nan M. Laird ; James H. Ware |
| 250 | |a 2. ed. | ||
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| 650 | 4 | |a Regression analysis | |
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| adam_text | Titel: Applied longitudinal analysis
Autor: Fitzmaurice, Garrett M
Jahr: 2011
Contents
Preface xvii
Preface to First Edition xxi
Acknowledgments xxv
Part I Introduction to Longitudinal and Clustered Data
1 Longitudinal and Clustered Data 1
1.1 Introduction 1
1.2 Longitudinal and Clustered Data 2
1. 3 Examples 5
1.4 Regression Models for Correlated Responses 13
1.5 Organization of the Book 16
1.6 Further Reading 18
2 Longitudinal Data: Basic Concepts 19
2.1 Introduction 19
2.2 Objectives of Longitudinal A nalysis 19
2.3 Defining Features of Longitudinal Data 22
vii
viii CONTENTS
2.4 Example: Treatment of Lead-Exposed Children Dial 31
2.5 Sources of Correlation in Longitudinal Ditto 36
2.6 Further Reading 44
Problems 44
Part II Linear Models for Longitudinal Continuous Data
3 Overview of Linear Models for Longitudinal Data 49
5.1 Introduction 4l
3.2 Notation and Distributional Assumptions s
3.3 Simple Descriptive Methods nf Analysis 62
3.4 Modeling the Mean 72
3.5 Modeling the Covariance 74
3.6 Historical Approaches 76
3.7 Further Reading 86
4 Estimation and Statistical Inference 89
4.1 Introduction 89
4.2 Estimation: Maximum Likelihood 90
4.3 Missing Data Issues 94
4.4 Statistical Inference 96
4.5 Restricted Maximum Likelihood IREMLi Estimation 101
4.6 Further Reading 104
5 Modeling the Mean: Analyzing Response Profiles 105
5.1 Introduction 105
5.2 Hypotheses Concerning Response Profiles 107
5.3 General Linear Model Formulation 112
5.4 Case Study 117
5.5 One-Degree-of-Freedom Tests for Group hx Tune
Interaction 120
5.6 Adjustment for Baseline Response 124
5.7 Alternative Methods of Adjusting for Baseline
Response* 128
CONTENTS ix
5.8 Strengths and Weaknesses of Analyzing Response
Profiles 134
5.9 Computing: Analyzing Response Profiles
Using PROC MIXED in SAS 136
5.10 Further Reading 140
Problems 140
Modeling the Mean: Parametric Curves 143
6.1 Introduction 143
6.2 Polynomial Trends in Time 144
6.5 Linear Splines 149
6.4 General Linear Model Formulation 152
6.5 Case Studies 154
6.6 Computing: Fitting Parametric Curves
Using PROC MIXED in SAS 161
6.7 Further Reading 162
Problems 163
Modeling the Covariance 165
7.1 Introduction 165
7.2 Implications of Correlation among Longitudinal Data 166
7.3 Unstructured Covariance 168
7.4 Covariance Pattern Models 169
7.5 Choice among Covariance Pattern Models 175
7.6 Case Study 180
7.7 Discussion: Strengths and Weaknesses of Covariance
Pattern Models 183
7.8 Computing: Fitting Covariance Pattern Models
Using PROC MIXED in SAS 184
7.9 Further Reading 186
Problems 186
Linear Mixed Effects Models 189
8.1 Introduction 189
8.2 Linear Mixed Effects Models 194
8.3 Random Effects Covariance Structure 201
8.4 Two-Stage Random Effects Formulation 203
8.5 Choice among Random Effects Covariance Models 208
8.6 Prediction of Random Effects 209
CONTENTS
8.7 Prediction and Shrinkage* 211
8.8 Case Studies 213
8.9 Computing: Fitting Linear Mixed Effects Models
Using PROC MIXED in SAS 234
8.10 Further Reading 237
Problems 237
Fixed Effects versus Random Effects Models 241
9.1 Introduction 241
9.2 Linear Fixed Effects Models 241
9.3 Fixed Effects versus Random Effects:
Bias-Variance Trade-off 246
9.4 Resolving the Dilemma of Choosing Between
Fixed and Random Effects Models 249
9.5 Longitudinal and Cross-sectional Information 252
9.6 Case Study 255
9.7 Computing: Fitting Linear Fixed Effects Models
Using PROC GLM in SAS 258
9.8 Computing: Decomposition of Between-Subject and
Within-Subject Effects Using PROC MIXED in SAS 260
9.9 Further Reading 262
Problems 262
10 Residual Analyses and Diagnostics 265
10.1 Introduction 265
10.2 Residuals 265
10.3 Transformed Residuals 266
10.4 Aggregating Residuals 269
10.5 Semi-Variogram 272
10.6 Case Study 273
10.7 Summary 285
10.8 Further Reading 286
Problems 287
CONTENTS XI
Part III Generalized Linear Models for Longitudinal Data
11 Review of Generalized Linear Models 291
11.1 Introduction 291
11.2 Salient Features of Generalized Linear Models 292
11.3 Illustrative Examples 297
11.4 Ordinal Regression Models 310
11.5 Overdispersion 319
11.6 Computing: Fitting Generalized Linear Models
Using PROC GENMOD in SAS 324
11.7 Overview of Generalized Linear Models* 327
11.8 Further Reading 335
Problems 336
12 Marginal Models: Introduction and Overview 341
12.1 Introduction 341
12.2 Marginal Models for Longitudinal Data 342
12.3 Illustrative Examples of Marginal Models 346
12.4 Distributional Assumptions for Marginal Models* 351
12.5 Further Reading 352
13 Marginal Models: Generalized Estimating
Equations (GEE) 353
13.1 Introduction 353
13.2 Estimation of Marginal Models: Generalized
Estimating Equations 354
13.3 Residual Analyses and Diagnostics 361
13. 4 Case Studies 364
13.5 Marginal Models and Time-Varying Covariates 381
13.6 Computing: Generalized Estimating Equations
Using PROC GENMOD in SAS 385
13.7 Further Reading 390
Problems 391
14 Generalized Linear Mixed Effects Models 395
14.1 Introduction 395
14.2 Incorporating Random Effects in Generalized
Linear Models 396
xii CONTENTS
14.3 Interpretation of Regression Parameters 402
14.4 Overdispersion 409
14.5 Estimation and Inference 410
14.6 A Note on Conditional Maximum Likelihood 412
14.7 Case Studies 414
14.8 Computing: Fitting Generalized Linear Mixed Models
Using PROC GLIMMLX in SAS 429
14.9 Further Reading 433
Problems 434
15 Generalized Linear Mixed Effects Models: Approximate
Methods of Estimation 441
15.1 Introduction 441
15.2 Penalized Quasi-Likelihood 443
15.3 Marginal Quasi-Likelihood 445
15.4 Cautionary Remarks on the Use of PQL and MQL 446
15.5 Case Studies 452
15.6 Computing: Fitting GLMMs Using PROC GLIMMIX
in SAS 459
15.7 Basis of PQL and MQL Approximations* 466
15.8 Further Reading 470
Problems 471
16 Contrasting Marginal and Mixed Effects Models 473
16.1 Introduction AT3
16.2 Linear Models: A Special Case 473
16.3 Generalized Linear Models A1A
16.4 Simple Numerical Illustration 479
16.5 Case Study 480
16.6 Conclusion 484
16.7 Further Reading 486
CONTENTS Xiii
Part IV Missing Data and Dropout
17 Missing Data and Dropout: Overview of Concepts
and Methods 489
17.1 Introduction 489
17.2 Hierarchy of Missing Data Mechanisms 491
17.3 Implications for Longitudinal Analysis 499
17.4 Dropout 500
17.5 Common Approaches for Handling Dropout 506
17.6 Bias of Last Value Carried Forward Imputation* 511
17.7 Further Reading 513
18 Missing Data and Dropout: Multiple Imputation
and Weighting Methods 515
18.1 Introduction 515
18.2 Multiple Imputation 516
18.3 Inverse Probability Weighted Methods 526
18.4 Case Studies 531
18.5 Sandwich Variance Estimator Adjusting for
Estimation of Weights* 541
18.6 Computing: Multiple Imputation Using PROC MI
in SAS 542
18.7 Computing: Inverse Probability Weighted (IPW)
Methods in SAS 547
18.8 Further Reading 550
Part V Advanced Topics for Longitudinal and Clustered Data
19 Smoothing Longitudinal Data: Semiparametric Regression
Models 553
19.1 Introduction 553
19.2 PenalizedSplinesfor a Univariate Response 554
19.3 Case Study 558
19.4 Penalized Splines for Longitudinal Data 563
19.5 Case Study 565
Xiv CONTENTS
19.6 Fitting Smooth Curves to Individual
Longitudinal Data 570
19.7 Case Study 572
19.8 Computing: Fitting Smooth Curves
Using PROC MIXED in SAS 576
19.9 Further Reading 579
20 Sample Size and Power 581
20.7 Introduction 581
20.2 Sample Size for a Univariate Continuous Response 582
20.3 Sample Size for a Longitudinal Continuous Response 584
20.4 Sample Size for a Longitudinal Binary Response 598
20.5 Summary 604
20.6 Computing: Sample Size Calculation
Using Pseudo-Data 605
20.7 Further Reading 609
21 Repeated Measures and Related Designs 611
27.7 Introduction 611
27.2 Repeated Measures Designs 612
27.5 Multiple Source Data 616
27.4 Case Study 1: Repeated Measures Experiment 617
27.5 Case Study 2: Multiple Source Data 620
27.6 Summary 625
27.7 Further Reading 626
22 Multilevel Models 627
22.7 Introduction 627
22.2 Multilevel Data 628
22.3 Multilevel Linear Models 630
22.4 Multilevel Generalized Linear Models 641
22.5 Summary 651
22.6 Further Reading 652
CONTENTS XV
Appendix A Gentle Introduction to Vectors and Matrices 655
Appendix B Properties of Expectations and Variances 665
Appendix C Critical Points for a 50:50 Mixture
of Chi-Squared Distributions 669
References 671
Index 695
|
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| indexdate | 2024-12-20T15:57:17Z |
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| language | English |
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| title | Applied longitudinal analysis |
| title_auth | Applied longitudinal analysis |
| title_exact_search | Applied longitudinal analysis |
| title_full | Applied longitudinal analysis Garrett M. Fitzmaurice ; Nan M. Laird ; James H. Ware |
| title_fullStr | Applied longitudinal analysis Garrett M. Fitzmaurice ; Nan M. Laird ; James H. Ware |
| title_full_unstemmed | Applied longitudinal analysis Garrett M. Fitzmaurice ; Nan M. Laird ; James H. Ware |
| title_short | Applied longitudinal analysis |
| title_sort | applied longitudinal analysis |
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| topic_facet | Longitudinal method Regression analysis Multivariate analysis Medical statistics MATHEMATICS / Probability & Statistics / General Längsschnittuntersuchung |
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