Epidemiology and Medical Statistics

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<ul><li><p>HANDBOOK OF STATISTICS</p><p>VOLUME 27</p></li><li><p>Handbook of Statistics</p><p>VOLUME 27</p><p>General Editor</p><p>C.R. Rao</p><p>Amsterdam Boston Heidelberg London New York OxfordParis San Diego San Francisco Singapore Sydney Tokyo</p></li><li><p>Epidemiology and Medical Statistics</p><p>Edited by</p><p>C.R. RaoCenter for Multivariate Analysis</p><p>Department of Statistics, The Pennsylvania State University</p><p>University Park, PA, USA</p><p>J.P. MillerDivision of Biostatistics</p><p>School of Medicine, Washington University in St. Louis</p><p>St. Louis, MO, USA</p><p>D.C. RaoDivision of Biostatistics</p><p>School of Medicine, Washington University in St. Louis</p><p>St. Louis, MO, USA</p><p>Amsterdam Boston Heidelberg London New York OxfordParis San Diego San Francisco Singapore Sydney Tokyo</p><p>North-Holland is an imprint of Elsevier</p></li><li><p>North-Holland is an imprint of Elsevier</p><p>Radarweg 29, PO Box 211, 1000 AE Amsterdam, The Netherlands</p><p>Linacre House, Jordan Hill, Oxford OX2 8DP, UK</p><p>First edition 2008</p><p>Copyright r 2008 Elsevier B.V. All rights reserved</p><p>No part of this publication may be reproduced, stored in a retrieval system</p><p>or transmitted in any form or by any means electronic, mechanical, photocopying,</p><p>recording or otherwise without the prior written permission of the publisher</p><p>Permissions may be sought directly from Elseviers Science &amp; Technology Rights</p><p>Department in Oxford, UK: phone (+44) (0) 1865 843830; fax (+44) (0) 1865 853333;</p><p>email: permissions@elsevier.com. Alternatively you can submit your request online by</p><p>visiting the Elsevier web site at http://www.elsevier.com/locate/permissions, and selecting</p><p>Obtaining permission to use Elsevier material</p><p>Notice</p><p>No responsibility is assumed by the publisher for any injury and/or damage to persons</p><p>or property as a matter of products liability, negligence or otherwise, or from any use</p><p>or operation of any methods, products, instructions or ideas contained in the material</p><p>herein. Because of rapid advances in the medical sciences, in particular, independent</p><p>verification of diagnoses and drug dosages should be made</p><p>British Library Cataloguing in Publication Data</p><p>A catalogue record for this book is available from the British Library</p><p>Library of Congress Cataloging-in-Publication Data</p><p>A catalog record for this book is available from the Library of Congress</p><p>ISBN: 978-0-444-52801-8</p><p>ISSN: 0169-7161</p><p>Printed and bound in The Netherlands</p><p>08 09 10 11 12 10 9 8 7 6 5 4 3 2 1</p><p>For information on all North-Holland publications</p><p>visit our website at books.elsevier.com</p></li><li><p>Table of contents</p><p>Preface xiii</p><p>Contributors xv</p><p>Ch. 1. Statistical Methods and Challenges in Epidemiology andBiomedical Research 1Ross L. Prentice</p><p>1. Introduction 1</p><p>2. Characterizing the study cohort 3</p><p>3. Observational study methods and challenges 6</p><p>4. Randomized controlled trials 12</p><p>5. Intermediate, surrogate, and auxiliary outcomes 17</p><p>6. Multiple testing issues and high-dimensional biomarkers 18</p><p>7. Further discussion and the Womens Health Initiative example 21</p><p>References 22</p><p>Ch. 2. Statistical Inference for Causal Effects, With Emphasis onApplications in Epidemiology and Medical Statistics 28Donald B. Rubin</p><p>1. Causal inference primitives 28</p><p>2. The assignment mechanism 36</p><p>3. Assignment-based modes of causal inference 41</p><p>4. Posterior predictive causal inference 47</p><p>5. Complications 55</p><p>References 58</p><p>Ch. 3. Epidemiologic Study Designs 64Kenneth J. Rothman, Sander Greenland and Timothy L. Lash</p><p>1. Introduction 64</p><p>2. Experimental studies 65</p><p>3. Nonexperimental studies 73</p><p>4. Cohort studies 73</p><p>v</p></li><li><p>5. Case-control studies 84</p><p>6. Variants of the case-control design 97</p><p>7. Conclusion 104</p><p>References 104</p><p>Ch. 4. Statistical Methods for Assessing Biomarkers and AnalyzingBiomarker Data 109Stephen W. Looney and Joseph L. Hagan</p><p>1. Introduction 109</p><p>2. Statistical methods for assessing biomarkers 110</p><p>3. Statistical methods for analyzing biomarker data 126</p><p>4. Concluding remarks 143</p><p>References 144</p><p>Ch. 5. Linear and Non-Linear Regression Methods in Epidemiology andBiostatistics 148Eric Vittinghoff, Charles E. McCulloch, David V. Glidden and Stephen</p><p>C. Shiboski</p><p>1. Introduction 148</p><p>2. Linear models 151</p><p>3. Non-linear models 167</p><p>4. Special topics 176</p><p>References 182</p><p>Ch. 6. Logistic Regression 187Edward L. Spitznagel Jr.</p><p>1. Introduction 187</p><p>2. Estimation of a simple logistic regression model 188</p><p>3. Two measures of model fit 191</p><p>4. Multiple logistic regression 192</p><p>5. Testing for interaction 194</p><p>6. Testing goodness of fit: Two measures for lack of fit 195</p><p>7. Exact logistic regression 196</p><p>8. Ordinal logistic regression 201</p><p>9. Multinomial logistic regression 204</p><p>10. Probit regression 206</p><p>11. Logistic regression in casecontrol studies 207</p><p>References 209</p><p>Ch. 7. Count Response Regression Models 210Joseph M. Hilbe and William H. Greene</p><p>1. Introduction 210</p><p>2. The Poisson regression model 212</p><p>3. Heterogeneity and overdispersion 224</p><p>4. Important extensions of the models for counts 230</p><p>Table of contentsvi</p></li><li><p>5. Software 247</p><p>6. Summary and conclusions 250</p><p>References 251</p><p>Ch. 8. Mixed Models 253Matthew J. Gurka and Lloyd J. Edwards</p><p>1. Introduction 253</p><p>2. Estimation for the linear mixed model 259</p><p>3. Inference for the mixed model 261</p><p>4. Selecting the best mixed model 264</p><p>5. Diagnostics for the mixed model 268</p><p>6. Outliers 270</p><p>7. Missing data 270</p><p>8. Power and sample size 272</p><p>9. Generalized linear mixed models 273</p><p>10. Nonlinear mixed models 274</p><p>11. Mixed models for survival data 275</p><p>12. Software 276</p><p>13. Conclusions 276</p><p>References 277</p><p>Ch. 9. Survival Analysis 281John P. Klein and Mei-Jie Zhang</p><p>1. Introduction 281</p><p>2. Univariate analysis 282</p><p>3. Hypothesis testing 288</p><p>4. Regression models 295</p><p>5. Regression models for competing risks 310</p><p>References 317</p><p>Ch. 10. A Review of Statistical Analyses for Competing Risks 321Melvin L. Moeschberger, Kevin P. Tordoff and Nidhi Kochar</p><p>1. Introduction 321</p><p>2. Approaches to the statistical analysis of competing risks 324</p><p>3. Example 327</p><p>4. Conclusion 339</p><p>References 340</p><p>Ch. 11. Cluster Analysis 342William D. Shannon</p><p>1. Introduction 342</p><p>2. Proximity measures 344</p><p>3. Hierarchical clustering 350</p><p>4. Partitioning 355</p><p>5. Ordination (scaling) 358</p><p>Table of contents vii</p></li><li><p>6. How many clusters? 361</p><p>7. Applications in medicine 364</p><p>8. Conclusion 364</p><p>References 365</p><p>Ch. 12. Factor Analysis and Related Methods 367Carol M. Woods and Michael C. Edwards</p><p>1. Introduction 367</p><p>2. Exploratory factor analysis (EFA) 368</p><p>3. Principle components analysis (PCA) 375</p><p>4. Confirmatory factor analysis (CFA) 375</p><p>5. FA with non-normal continuous variables 379</p><p>6. FA with categorical variables 380</p><p>7. Sample size in FA 382</p><p>8. Examples of EFA and CFA 383</p><p>9. Additional resources 389</p><p>Appendix A 391</p><p>Appendix B 391</p><p>References 391</p><p>Ch. 13. Structural Equation Modeling 395Kentaro Hayashi, Peter M. Bentler and Ke-Hai Yuan</p><p>1. Models and identification 395</p><p>2. Estimation and evaluation 399</p><p>3. Extensions of SEM 410</p><p>4. Some practical issues 415</p><p>References 418</p><p>Ch. 14. Statistical Modeling in Biomedical Research: LongitudinalData Analysis 429Chengjie Xiong, Kejun Zhu, Kai Yu and J. Philip Miller</p><p>1. Introduction 429</p><p>2. Analysis of longitudinal data 431</p><p>3. Design issues of a longitudinal study 456</p><p>References 460</p><p>Ch. 15. Design and Analysis of Cross-Over Trials 464Michael G. Kenward and Byron Jones</p><p>1. Introduction 464</p><p>2. The two-period two-treatment cross-over trial 467</p><p>3. Higher-order designs 476</p><p>4. Analysis with non-normal data 482</p><p>5. Other application areas 485</p><p>6. Computer software 488</p><p>References 489</p><p>Table of contentsviii</p></li><li><p>Ch. 16. Sequential and Group Sequential Designs in Clinical Trials:Guidelines for Practitioners 491Madhu Mazumdar and Heejung Bang</p><p>1. Introduction 492</p><p>2. Historical background of sequential procedures 493</p><p>3. Group sequential procedures for randomized trials 494</p><p>4. Steps for GSD design and analysis 507</p><p>5. Discussion 508</p><p>References 509</p><p>Ch. 17. Early Phase Clinical Trials: Phases I and II 513Feng Gao, Kathryn Trinkaus and J. Philip Miller</p><p>1. Introduction 513</p><p>2. Phase I designs 514</p><p>3. Phase II designs 526</p><p>4. Summary 539</p><p>References 541</p><p>Ch. 18. Definitive Phase III and Phase IV Clinical Trials 546Barry R. Davis and Sarah Baraniuk</p><p>1. Introduction 546</p><p>2. Questions 548</p><p>3. Randomization 550</p><p>4. Recruitment 551</p><p>5. Adherence/sample size/power 552</p><p>6. Data analysis 554</p><p>7. Data quality and control/data management 558</p><p>8. Data monitoring 558</p><p>9. Phase IV trials 563</p><p>10. Dissemination trial reporting and beyond 564</p><p>11. Conclusions 565</p><p>References 565</p><p>Ch. 19. Incomplete Data in Epidemiology and Medical Statistics 569Susanne Rassler, Donald B. Rubin and Elizabeth R. Zell</p><p>1. Introduction 569</p><p>2. Missing-data mechanisms and ignorability 571</p><p>3. Simple approaches to handling missing data 573</p><p>4. Single imputation 574</p><p>5. Multiple imputation 578</p><p>6. Direct analysis using model-based procedures 581</p><p>7. Examples 584</p><p>8. Literature review for epidemiology and medical studies 586</p><p>9. Summary and discussion 587</p><p>Appendix A 588</p><p>Appendix B 592</p><p>References 598</p><p>Table of contents ix</p></li><li><p>Ch. 20. Meta-Analysis 602Edward L. Spitznagel Jr.</p><p>1. Introduction 602</p><p>2. History 603</p><p>3. The CochranMantelHaenszel test 604</p><p>4. Glasss proposal for meta-analysis 606</p><p>5. Random effects models 607</p><p>6. The forest plot 609</p><p>7. Publication bias 610</p><p>8. The Cochrane Collaboration 614</p><p>References 614</p><p>Ch. 21. The Multiple Comparison Issue in Health Care Research 616Lemuel A. Moye</p><p>1. Introduction 616</p><p>2. Concerns for significance testing 617</p><p>3. Appropriate use of significance testing 618</p><p>4. Definition of multiple comparisons 619</p><p>5. Rational for multiple comparisons 620</p><p>6. Multiple comparisons and analysis triage 621</p><p>7. Significance testing and multiple comparisons 623</p><p>8. Familywise error rate 625</p><p>9. The Bonferroni inequality 626</p><p>10. Alternative approaches 629</p><p>11. Dependent testing 631</p><p>12. Multiple comparisons and combined endpoints 635</p><p>13. Multiple comparisons and subgroup analyses 641</p><p>14. Data dredging 651</p><p>References 651</p><p>Ch. 22. Power: Establishing the Optimum Sample Size 656Richard A. Zeller and Yan Yan</p><p>1. Introduction 656</p><p>2. Illustrating power 658</p><p>3. Comparing simulation and software approaches to power 663</p><p>4. Using power to decrease sample size 672</p><p>5. Discussion 677</p><p>References 677</p><p>Ch. 23. Statistical Learning in Medical Data Analysis 679Grace Wahba</p><p>1. Introduction 679</p><p>2. Risk factor estimation: penalized likelihood estimates 681</p><p>3. Risk factor estimation: likelihood basis pursuit and the LASSO 690</p><p>Table of contentsx</p></li><li><p>4. Classification: support vector machines and related estimates 693</p><p>5. Dissimilarity data and kernel estimates 700</p><p>6. Tuning methods 704</p><p>7. Regularization, empirical Bayes, Gaussian processes priors, and</p><p>reproducing kernels 707</p><p>References 708</p><p>Ch. 24. Evidence Based Medicine and Medical Decision Making 712Dan Mayer</p><p>1. The definition and history of evidence based medicine 712</p><p>2. Sources and levels of evidence 715</p><p>3. The five stage process of EBM 717</p><p>4. The hierarchy of evidence: study design and minimizing bias 718</p><p>5. Assessing the significance or impact of study results: Statistical significance and</p><p>confidence intervals 721</p><p>6. Meta-analysis and systematic reviews 722</p><p>7. The value of clinical information and assessing the usefulness</p><p>of a diagnostic test 722</p><p>8. Expected values decision making and the threshold approach</p><p>to diagnostic testing 726</p><p>9. Summary 727</p><p>10. Basic principles 727</p><p>References 728</p><p>Ch. 25. Estimation of Marginal Regression Models with MultipleSource Predictors 730Heather J. Litman, Nicholas J. Horton, Bernardo Hernandez and</p><p>Nan M. Laird</p><p>1. Introduction 730</p><p>2. Review of the generalized estimating equations approach 732</p><p>3. Maximum likelihood estimation 735</p><p>4. Simulations 737</p><p>5. Efficiency calculations 740</p><p>6. Illustration 741</p><p>7. Conclusion 743</p><p>References 745</p><p>Ch. 26. Difference Equations with Public Health Applications 747Asha Seth Kapadia and Lemuel A. Moye</p><p>1. Introduction 747</p><p>2. Generating functions 748</p><p>3. Second-order nonhomogeneous equations and generating functions 750</p><p>4. Example in rhythm disturbances 752</p><p>5. Follow-up losses in clinical trials 758</p><p>6. Applications in epidemiology 765</p><p>References 773</p><p>Table of contents xi</p></li><li><p>Ch. 27. The Bayesian Approach to Experimental Data Analysis 775Bruno Lecoutre</p><p>Preamble: and if you were a Bayesian without knowing it? 775</p><p>1. Introduction 776</p><p>2. Frequentist and Bayesian inference 778</p><p>3. An illustrative example 783</p><p>4. Other examples of inferences about proportions 795</p><p>5. Concluding remarks and some further topics 803</p><p>References 808</p><p>Subject Index 813</p><p>Handbook of Statistics Contents of Previous Volumes 823</p><p>Table of contentsxii</p></li><li><p>Preface</p><p>The history of statistics suggests two important lessons. First and foremost,methodological research flourished in the hands of those who were not onlyhighly focused on problems arising from real data, but who were themselvesimmersed in generating highly valuable data, like the late Sir Ronald A. Fisher.Second, although theoretical statistics research can be nurtured in isolation, onlyan applied orientation has made it possible for such efforts to reach new heights.Throughout the history of statistics, the most innovative and path breakingmethodological advances have come out when brilliant statisticians haveconfronted the practical challenges arising from real data. The computationalrevolution has certainly made many of the most difficult computational algo-rithms readily available for common use, which in turn made the data-centricapproach to methodological innovation sustainable. That the data-centricapproach enriched statistics itself is amply demonstrated by the varied applica-tions of statistical methods in epidemiology and medical studies.</p><p>This volume presents a collection of chapters that focus on applications ofstatistical methods in epidemiology and medical statistics. Some of them are timetested and proven methods with long-track records while others bring the latestmethodologies where their promise and relevance compensate for their relativelyshort-track records. This volume includes 27 chapters. In Chapter 1, Prenticeaddresses methodological challenges in epidemiology and biomedical research ingeneral. Conceptually, the remaining 26 chapters may be divided into three cat-egories: standard and traditional methods, relatively more advanced methods,and recent methods. These less traditional methods generally provide models withgreater fidelity to the underlying data generation mechanisms at the expense ofmore computational complexity.</p><p>The first category includes 10 chapters. Chapter 3 by Rothman et al. discussesepidemiological study designs. Chapter 5 by Vittinghoff et al. discusses linear andnon-linear regression methods. Spitznagel discusses logistic regression methods inChapter 6. In Chapter 8, Gurka and Edwards review mixed models. In Chapter11, Shannon discusses cluster analysis. Woods and Edwards discuss factor anal-ysis in Chapter 12. A few chapters are devoted to clinical trials. Chapter 15 byKenward and Jones discusses design and analysis of cross-over trials, Chapter 17by Gao et al. discusses early phase clinical trials (I and II), and Chapter 18 byDavis and Baraniuk discusses definitive phases III and IV clinical trials. InChapter 22, Zeller and Yan discuss th...</p></li></ul>


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