Applied longitudinal analysis

Library	Item Barcode	Call Number	Material Type	Item Category 1	Status
Searching... PSZ JB	30000010063510	QA278 F57 2004	Open Access Book	Book	Searching... Unknown
Searching... PSZ JB	30000010070395	QA278 F57 2004	Open Access Book	Book	Searching... Unknown

A rigorous, systematic presentation of modern longitudinal analysis
Longitudinal studies, employing repeated measurement of subjects over time, play a prominent role in the health and medical sciences as well as in pharmaceutical studies. An important strategy in modern clinical research, they provide valuable insights into both the development and persistence of disease and those factors that can alter the course of disease development.
Written at a technical level suitable for researchers and graduate students, Applied Longitudinal Analysis provides a rigorous and comprehensive description of modern methods for analyzing longitudinal data. Focusing on General Linear and Mixed Effects Models for continuous responses, and extensions of Generalized Linear Models for discrete responses, the authors discuss in detail the relationships among these different models, including their underlying assumptions and relative merits. The book features:
* A focus on practical applications, utilizing a wide range of examples drawn from real-world studies
* Coverage of modern methods of regression analysis for correlated data
* Analyses utilizing SAS(r)
* Multiple exercises and "homework" problems for review
An accompanying Web site features twenty-five real data sets used throughout the text, in addition to programming statements and selected computer output for the examples.

Author Notes

GARRETT M. FITZMAURICE, ScD , is Associate Professor of Biostatistics at the Harvard School of Public Health.

NAN M. LAIRD, PhD, is Professor of Biostatistics at the Harvard School of Public Health.

JAMES H. WARE, PhD , is Frederick Mosteller Professor of Biostatistics and Dean for Academic Affairs at the Harvard School of Public Health.
All three authors are Fellows of the American Statistical Association and members of the International Statistical Institute.

Reviews 1

Choice Review

Written to address the paucity of graduate-level works on longitudinal and clustered data in the health and medical sciences, Fitzmaurice and colleagues (all, Harvard Univ.) provide solid coverage of both technique and the conceptual underpinnings of the material at hand. The 17 chapters rely on real data from the field to illustrate the applications and include well-founded, practical descriptions. (These data sets can be downloaded from the book's Web site.) Issues regarding missing data are addressed along with linear, covariance pattern, linear mixed effects, and marginal models. Naturally, residual analysis and related diagnostic concerns are also explored. Most chapters include problem sets, generally requiring statistical software for their completion (with SAS recommended by the authors). Of special value are case studies throughout the book, effectively illustrating the presented techniques and concepts. Appendixes include a brief review of essential matrix techniques, properties of expectations and variances, and critical points for a 50:50 mixture of chi-squared distributions. The authors' decision to include introductory comments at the start of each chapter and descriptive comments of sources for additional readings for each chapter lends additional solidity to the presentation. ^BSumming Up: Recommended. Graduate students through professionals. N. W. Schillow Lehigh Carbon Community College

Preface	p. xv
Acknowledgments	p. xix
Part I Introduction to Longitudinal and Clustered Data
1 Longitudinal and Clustered Data	p. 1
1.1 Introduction	p. 1
1.2 Longitudinal and Clustered Data	p. 2
1.3 Examples	p. 5
1.4 Regression Models for Correlated Responses	p. 13
1.5 Organization of This Book	p. 16
1.6 Further Reading	p. 18
2 Longitudinal Data: Basic Concepts	p. 19
2.1 Introduction	p. 19
2.2 Objectives of Longitudinal Analysis	p. 19
2.3 Defining Features of Longitudinal Data	p. 22
2.4 Example: Treatment of Lead-Exposed Children Trial	p. 31
2.5 Sources of Correlation in Longitudinal Data	p. 36
2.6 Further Reading	p. 44
Problems	p. 44
Part II Linear Models for Longitudinal Continuous Data
3 Overview of Linear Models for Longitudinal Data	p. 49
3.1 Introduction	p. 49
3.2 Notation and Distributional Assumptions	p. 50
3.3 Simple Descriptive Methods of Analysis	p. 62
3.4 Modelling the Mean	p. 71
3.5 Modelling the Covariance	p. 73
3.6 Historical Approaches	p. 76
3.7 Further Reading	p. 86
4 Estimation and Statistical Inference	p. 87
4.1 Introduction	p. 87
4.2 Estimation: Maximum Likelihood	p. 88
4.3 Missing Data Issues	p. 92
4.4 Statistical Inference	p. 94
4.5 Restricted Maximum Likelihood (REML) Estimation	p. 99
4.6 Further Reading	p. 102
5 Modelling the Mean: Analyzing Response Profiles	p. 103
5.1 Introduction	p. 103
5.2 Hypotheses Concerning Response Profiles	p. 105
5.3 General Linear Model Formulation	p. 110
5.4 Case Study	p. 115
5.5 One-Degree-of-Freedom Tests for Group by Time Interaction	p. 118
5.6 Adjustment for Baseline Response	p. 122
5.7 Alternative Methods of Adjusting for Baseline Response	p. 126
5.8 Strengths and Weaknesses of Analyzing Response Profiles	p. 132
5.9 Computing: Analyzing Response Profiles Using PROC MIXED in SAS	p. 134
5.10 Further Reading	p. 138
Problems	p. 138
6 Modelling the Mean: Parametric Curves	p. 141
6.1 Introduction	p. 141
6.2 Polynomial Trends in Time	p. 142
6.3 Linear Splines	p. 147
6.4 General Linear Model Formulation	p. 150
6.5 Case Studies	p. 152
6.6 Computing: Fitting Parametric Curves Using PROC MIXED in SAS	p. 159
6.7 Further Reading	p. 160
Problems	p. 161
7 Modelling the Covariance	p. 163
7.1 Introduction	p. 163
7.2 Implications of Correlation among Longitudinal Data	p. 164
7.3 Unstructured Covariance	p. 166
7.4 Covariance Pattern Models	p. 167
7.5 Choice among Covariance Pattern Models	p. 173
7.6 Case Study	p. 178
7.7 Discussion: Strengths and Weaknesses of Covariance Pattern Models	p. 181
7.8 Computing: Fitting Covariance Pattern Models Using PROC MIXED in SAS	p. 182
7.9 Further Reading	p. 184
Problems	p. 184
8 Linear Mixed Effects Models	p. 187
8.1 Introduction	p. 187
8.2 Linear Mixed Effects Models	p. 192
8.3 Random Effects Covariance Structure	p. 198
8.4 Two-Stage Random Effects Formulation	p. 200
8.5 Choice among Random Effects Covariance Models	p. 205
8.6 Prediction of Random Effects	p. 206
8.7 Prediction and Shrinkage	p. 208
8.8 Case Studies	p. 210
8.9 Computing: Fitting Linear Mixed Effects Models Using PROC MIXED in SAS	p. 231
8.10 Further Reading	p. 233
Problems	p. 234
9 Residual Analyses and Diagnostics	p. 237
9.1 Introduction	p. 237
9.2 Residuals	p. 237
9.3 Transformed Residuals	p. 238
9.4 Semi-Variogram	p. 241
9.5 Case Study	p. 242
9.6 Summary	p. 251
9.7 Further Reading	p. 252
Problems	p. 253
Part III Generalized Linear Models for Longitudinal Data
10 Review of Generalized Linear Models	p. 257
10.1 Introduction	p. 257
10.2 Salient Features of Generalized Linear Models	p. 258
10.3 Illustrative Examples	p. 263
10.4 Computing: Fitting Generalized Linear Models Using PROC GENMOD in SAS	p. 276
10.5 Overview of Generalized Linear Models	p. 279
10.6 Further Reading	p. 287
Problems	p. 287
11 Marginal Models: Generalized Estimating Equations (GEE)	p. 291
11.1 Introduction	p. 291
11.2 Marginal Models for Longitudinal Data	p. 292
11.3 Estimation for Marginal Models: Generalized Estimating Equations	p. 299
11.4 Case Studies	p. 305
11.5 Computing: Generalized Estimating Equations Using PROC GENMOD in SAS	p. 316
11.6 Distributional Assumptions for Marginal Models	p. 319
11.7 Further Reading	p. 321
Problems	p. 321
12 Generalized Linear Mixed Effects Models	p. 325
12.1 Introduction	p. 325
12.2 Incorporating Random Effects in Generalized Linear Models	p. 326
12.3 Interpretation of Regression Parameters	p. 331
12.4 Estimation and Inference	p. 338
12.5 Case Studies	p. 340
12.6 Computing: Fitting Generalized Linear Mixed Models Using PROC NLNIXED in SAS	p. 351
12.7 Further Reading	p. 354
Problems	p. 355
13 Contrasting Marginal and Mixed Effects Models	p. 359
13.1 Introduction	p. 359
13.2 Linear Models: A Special Case	p. 359
13.3 Generalized Linear Models	p. 360
13.4 Simple Numerical Illustration	p. 364
13.5 Case Study	p. 365
13.6 Conclusion	p. 369
13.7 Further Reading	p. 371
Part IV Advanced Topics for Longitudinal and Clustered Data
14 Missing Data and Dropout	p. 375
14.1 Introduction	p. 375
14.2 Hierarchy of Missing Data Mechanisms	p. 377
14.3 Implications for Longitudinal Analysis	p. 384
14.4 Dropout	p. 386
14.5 Common Approaches for Handling Dropout	p. 391
14.6 Case Study	p. 397
14.7 Further Reading	p. 400
15 Some Aspects of the Design of Longitudinal Studies	p. 401
15.1 Introduction	p. 401
15.2 Sample Size and Power	p. 401
15.3 Interpretation of Stochastic Time-Varying Covariates	p. 414
15.4 Longitudinal and Cross-Sectional Information	p. 418
15.5 Further Reading	p. 422
16 Repeated Measures and Related Designs	p. 425
16.1 Introduction	p. 425
16.2 Repeated Measures Designs	p. 426
16.3 Multiple Source Data	p. 430
16.4 Case Study 1: Repeated Measures Experiment	p. 431
16.5 Case Study 2: Multiple Source Data	p. 434
16.6 Summary	p. 439
16.7 Further Reading	p. 440
17 Multilevel Models	p. 441
17.1 Introduction	p. 441
17.2 Multilevel Data	p. 442
17.3 Multilevel Linear Models	p. 444
17.4 Multilevel Generalized Linear Models	p. 455
17.5 Summary	p. 465
17.6 Further Reading	p. 466
Appendix A Gentle Introduction to Vectors and Matrices	p. 469
Appendix B Properties of Expectations and Variances	p. 479
Appendix C Critical Points for a 50:50 Mixture of Chi-Squared Distributions	p. 483
References	p. 485
Index	p. 501

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