Modeling and analysis of longitudinal data:
Gespeichert in:
| Weitere Verfasser: | , , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
London ; San Diego ; Cambridge, MA
Academic Press
[2024]
|
| Schriftenreihe: | Handbook of statistics
volume 50 |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | xvii, 342 Seiten Illustrationen, Diagramme 24 cm |
| ISBN: | 9780443136511 |
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| 245 | 1 | 0 | |a Modeling and analysis of longitudinal data |c edited by Donald E. K. Martin, North Carolina State University, Raleigh, NC, United States, Arni S.R. Srinivasa Rao, Medical College of Georgia, Augusta, GA, United States, C.R. Rao, AIMSCS, University of Hyderabad Campus, Hyderabad, India |
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Contents Contributors Preface xiii xv Part I Foundations 1. Multivariate and shared parameter mixed-effects models for intensive longitudinal data 1 3 Donald Hedeker, Juned Siddique, Xingruo Zhang, and Bonnie Spring 1. 2. 3. 4 Multivariate mixed-effects models 4 Example 7 3.1 Bivariate mixed model 9 3.2 Bivariate shared parameter mixed model 12 3.3 Bivariate shared parameter mixedmodel with interactions 16 3.4 Bivariate shared parameter mixed model with random effect interactions 20 4. Discussion 26 Appendix A. SAS PROC NLMIXED code 27 Appendix B. STAN code 32 Acknowledgments 34 References 34 2. Introduction Hierarchical and incomplete data Geert Verbeke and Geert Molenberghs Introduction Case study: Age-related macular degeneration trial Modeling tools for longitudinal data 3.1 Linear models for Gaussian data 3.2 Models for non-Gaussian outcomes 4. Marginalization and marginal interpretationof hierarchical models 5. Incomplete data 5.1 Formalism 5.2 Weighted generalized estimating equations 1. 2. 3. 37 37 39 40 41 42 46 47 49 50 V
Contents vi 5.3 Double robustness 5.4 Multiple imputation 6. Analysis of the ARMD trial 7. Extensions 7.1 Multivariate longitudinal data 7.2 Joint models for longitudinal and time-to-event data 7.3 Issues with large data sets 7.4 Closed forms and restoring balance 8. Flexibly accommodating correlation and overdispersion 9. Concluding remarks References 3. Modeling longitudinal trends in event-related potentials 51 51 56 66 66 68 68 69 70 70 71 77 Damla Senturk and Aaron Scheffler 1. Introduction 77 2. An implicit learning paradigm 78 3. What is an ERP? 80 4. Preprocessing ERPs 81 5. Enhancing signal-to-noise ratio for longitudinal modeling 81 6. Modeling longitudinal trends 85 6.1 Mixed-effects models 85 6.2 Clustering longitudinal trends 89 6.3 Lower-dimensional representation via functional principal components analysis 94 Acknowledgment 102 References 102 Part II Further methods and models 4. Longitudinal data analysis by hierarchical state space models Ziyue Liu and Wensheng Guo 1. Introduction 2. Linear GaussianSSMs 2.1 The classical fixed effects and random effects 2.2 Smoothness priors and smoothing splines 2.3 Ordinary differential equations based models 2.4 Time series models 2.5 Dynamic factor models 2.6 Multiprocess dynamic linear models 3. Hierarchical statespace models 3.1 The general model 3.2 The vector form 107 109 109 110 111 111 112 113 114 115 115 115 116
Contents Estimation, inference and prediction 4.1 Linear mixed-effects model representation 4.2 State estimation and the likelihood function 4.3 BLUP and confidence intervals 4.4 Parameter estimation 4.5 Inference and model building 4.6 Prediction into the future domain 4.7 Software implementations 5. Applications 5.1 Cortisol dynamics 5.2 State-wise unemployment rate of the United States 6. Extensions to bivariate HSSMs 6.1 The general model 6.2 Modeling the relationships 6.3 Estimation and inference 6.4 Application to ACTH and cortisol 6.5 Estimated circadian rhythms 7. Extensions to nonlinear non-Gaussian HSSMs 7.1 The nonlinear non-Gaussian HSSMs 7.2 SMC algorithms for a single subject 7.3 SAEM 7.4 The SMC-Gibbs-SAEM algorithm 7.5 Standard errors 7.6 Numeric example: Poisson functional mixed-effects model 8. Bibliographical notes Sections 3 and 4 Section 6 Section 7 References 4. 5. Latent state-trait analysis vii 117 117 118 119 119 119 120 122 122 122 126 129 129 130 132 132 136 137 138 139 143 143 145 146 148 148 148 149 150 155 Christian Ceiser Introduction Background Latent variables in LST theory Consistency, occasion specificity, and reliability The multitrait-multistate (MTMS) model Illustrative application of the MTMS model The Singletrait-Multistate Model with m - 1 Indicator-Specific Factors 8. Illustrative application of the STMS-IS model 9. Discussion 9.1 Overview of other LST approaches and extensions Appendix A1: Mplus software code for fitting the MTMS model to the MRT data 169 1. 2. 3. 4. 5. 6. 7. 155 156 156 157 160 161 163 165 166 166
viii Contents Appendix A2: Appendix B1: Appendix B2: Lavaan software code for fitting the MTMS model to the MRT data 169 Mplus syntax for fitting the STMS-IS model to the MRT dataexample 170 Lavaan syntax for fitting the STMS-IS model to the MRT dataexample 170 References 6. Recent advances in longitudinal data analysis Liya Fu, You-Gan Wang, and Jinran Wu 1. Introduction 1.1 Notation 1.2 Covariance models and random effects 1.3 Multilevel models 2. Covariance selection 2.1 Covariance selection:Correlation structures 2.2 Covariance selection: Variance functions 3. Variable selection 3.1 Optimal subset methods 3.2 Regularization methods 3.3 M-estimation-based robust variable selection methods 3.4 Rank regression 3.5 Quantile regression 4. Machine learning approaches 4.1 Tree-based methods 4.2 Kernel models 4.3 Neural networks 4.4 Predictive performance comparison 5. Discussion Acknowledgments References 171 173 173 175 176 177 179 179 184 185 186 188 191 197 200 204 204 207 210 213 214 215 216 Part III Applications 7. 223 Government as population: Demographic perspectives on the United States legislative, judicial and executive branches, 1789-2020 225 James R. Carey, Brinsley Eriksen, and Arni S.R. Srinivasa Rao 1. Introduction 2. Data and sources 3. Population perspectives 3.1 Demography of U.S. in 1789 3.2 Demographic predictions from constitution 3.3 Observed historical patterns 226 228 229 229 229 230
Contents ix Applications and projections 4.1 Stationary population identity 4.2 Probabilities of feet first government exits 4.3 Territoriality-based representation 5. The political destiny of government demography 5.1 Territorially-based representation 5.2 The emerging gerontocracy 6. Conclusions Acknowledgments References 240 240 247 252 258 259 259 260 262 262 Beginnings: Formation and growth of natural phenomena out of Fisher information 267 4. 8. B. Roy Frieden 1. 2. 3. 4. 5. 6. Nature, as evolving, communicating systems: Essential role of background Fisher information 268 1.1 Vital model assumption 269 1.2 A recent verification 270 1.3 Introduction: How does the classic Fisher information-measure I advance scientific knowledge, which often is quantum? (Note: here "classic" does not mean "non-quantum," as below; I Is intrinsically a quantum-based, phase-sensitive measure) 270 1.4 Problem definition: And on prior, related work of other authors 271 1.5 Commonality of growth effects as "channels" of information 271 1.6 Net working principle 273 1.7 How does information level J arise? 274 1.8 Scope: Growth from "nothing" over an information channel 276 1.9 Wheeler thesis of "creation" as a form of active observation 278 What has past use of the MFI principle shown? 278 2.1 Physical properties of MFI solutions 279 2.2 Central role played by the system state parameter value a 280 Information aspects of system coordinates and parameters 281 3.1 Essential role played by the data y and system state value a 281 3.2 Universal role played by the classic Fisher information 281
Nature of the output: Schrodinger's dilemma 283 4.1 Wide scope of MFI approach 284 Concept of system "complexity" 285 Principle of maximum Fisher information (MFI) 286 6.1 Cramer-Rao (C-R) inequality: A quest for minimizing error in knowledge of a system state a 286 6.2 Negligible role played by the bias b(a} 287 6.3 Communication channel model 288 6.4 What does minimizing the error Eq. (6) accomplish? 288
Contents X To review 288 Null role played by specific form chosen for estimator function 289 Definition of Fisher information (Fl) I 289 7.1 Case of one-dimensional scalar-coordinate x on interval (c,7) 289 7.2 Four-space scalar cases 290 7.3 Four-space tensor cases 290 7.4 Application to quantum-gravitation 291 7.5 Review of the forms expressing system fisher information level for a given space 291 7.6 Minimization of the error deficit; creation of units: The "information demon": MFI principle 292 7.7 On coefficient к 293 7.8 Digression: An information demon 293 7.9 Fixed nature of ever-present information level J 293 Summary to this point 295 8.1 Mathematical consistency: Nature as optimized states of reality 295 MFI view of the fundamental physical constants per se as statistical entities 296 Discussion 296 Information channels: Basics of MFI principle 300 11.1 The MFI view of an information channel 300 11.2 Coefficient к measures efficiency of information transferal from "input" to "output" spaces 302 11.3 Consistency of the variational effect 303 11.4 What does information principle (9a and 9b) accomplish? 303 11.5 Interrelation of system developments 304 11.6 Discussion 304 11.7 Nature of the information flow: Past quantum applications: A predator-prey "channel" 305 Application: Early growth laws p(t) for viruses, cancer and the cosmos 306 12.1 Regarding finite onset time t0 of the cosmos 306 Regarding the fundamental need for well-defined Planck constants 307 On early growth of cancer, or virus, asquantum-based 309 Growth laws of viruses: Of cancer 309 15.1 Early
viral growth 309 15.2 Early cancer growth 311 Experimental verification 312 16.1 Further note, in this regard 312 A sketch of past work on cosmic origins relating to the preceding theory of fluctuations 312 Possible mechanisms for a perpetualinputinformation level J 313 6.5 6.6 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18.
Contents 19. xi Comparison for early growth of viruses, cancer, the cosmos 19.1 An appreciation Appendix A Appendix: The MFI (or EPI) principle is satisfied by power-law solutions for q(t), p(t) A.1 Key physical system requirement A.2 On the perpetuity of information source level; A.3 Higgs boson, and some unresolved issues A.4 Nature of the observer A.5 Synopsis to this point A.6 Digression A.7 Continuation A.8 Finding the weight function At) A.9 Correspondence with Robertson-Walker scale factor R A. 10 Values of emergence time delays e strongly influence subsequent growth formations 324 Appendix В Appendix: The required exponent in power-law solution p(t) is generally the Fibonacci constant 0 324 B.1 Full use of the MFI approach: Establishing a "universal" power-law exponent а 325 References Further reading Index 314 315 315 317 318 319 320 320 321 321 322 323 330 334 335 |
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| spelling | Modeling and analysis of longitudinal data edited by Donald E. K. Martin, North Carolina State University, Raleigh, NC, United States, Arni S.R. Srinivasa Rao, Medical College of Georgia, Augusta, GA, United States, C.R. Rao, AIMSCS, University of Hyderabad Campus, Hyderabad, India London ; San Diego ; Cambridge, MA Academic Press [2024] © 2024 xvii, 342 Seiten Illustrationen, Diagramme 24 cm txt rdacontent n rdamedia nc rdacarrier Handbook of statistics volume 50 Datenanalyse (DE-588)4123037-1 gnd rswk-swf Längsschnittuntersuchung (DE-588)4034036-3 gnd rswk-swf (DE-588)4143413-4 Aufsatzsammlung gnd-content Datenanalyse (DE-588)4123037-1 s Längsschnittuntersuchung (DE-588)4034036-3 s DE-604 Martin, Donald E. K. (DE-588)1322405239 edt Rao, Arni S. R. Srinivasa (DE-588)1143256220 edt Rao, Calyampudi Radhakrishna 1920-2023 (DE-588)119285924 edt Handbook of statistics volume 50 (DE-604)BV000002510 50 Digitalisierung UB Bamberg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=034929691&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
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| title_full | Modeling and analysis of longitudinal data edited by Donald E. K. Martin, North Carolina State University, Raleigh, NC, United States, Arni S.R. Srinivasa Rao, Medical College of Georgia, Augusta, GA, United States, C.R. Rao, AIMSCS, University of Hyderabad Campus, Hyderabad, India |
| title_fullStr | Modeling and analysis of longitudinal data edited by Donald E. K. Martin, North Carolina State University, Raleigh, NC, United States, Arni S.R. Srinivasa Rao, Medical College of Georgia, Augusta, GA, United States, C.R. Rao, AIMSCS, University of Hyderabad Campus, Hyderabad, India |
| title_full_unstemmed | Modeling and analysis of longitudinal data edited by Donald E. K. Martin, North Carolina State University, Raleigh, NC, United States, Arni S.R. Srinivasa Rao, Medical College of Georgia, Augusta, GA, United States, C.R. Rao, AIMSCS, University of Hyderabad Campus, Hyderabad, India |
| title_short | Modeling and analysis of longitudinal data |
| title_sort | modeling and analysis of longitudinal data |
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