Statistical Advances in the Biomedical Sciences: Clinical Trials, Epidemiology, Survival Analysis, and Bioinformatics / Edition 1

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The Most Comprehensive and Cutting-Edge Guide to Statistical Applications in Biomedical Research

With the increasing use of biotechnology in medical research and the sophisticated advances in computing, it has become essential for practitioners in the biomedical sciences to be fully educated on the role statistics plays in ensuring the accurate analysis of research findings. Statistical Advances in the Biomedical Sciences explores the growing value of statistical knowledge in the management and comprehension of medical research and, more specifically, provides an accessible introduction to the contemporary methodologies used to understand complex problems in the four major areas of modern-day biomedical science: clinical trials, epidemiology, survival analysis, and bioinformatics.

Composed of contributions from eminent researchers in the field, this volume discusses the application of statistical techniques to various aspects of modern medical research and illustrates how these methods ultimately prove to be an indispensable part of proper data collection and analysis. A structural uniformity is maintained across all chapters, each beginning with an introduction that discusses general concepts and the biomedical problem under focus and is followed by specific details on the associated methods, algorithms, and applications. In addition, each chapter provides a summary of the main ideas and offers a concluding remarks section that presents novel ideas, approaches, and challenges for future research.

Complete with detailed references and insight on the future directions of biomedical research, Statistical Advances in the Biomedical Sciences provides vital statistical guidance to practitioners in the biomedical sciences while also introducing statisticians to new, multidisciplinary frontiers of application. This text is an excellent reference for graduate- and PhD-level courses in various areas of biostatistics and the medical sciences and also serves as a valuable tool for medical researchers, statisticians, public health professionals, and biostatisticians.

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Editorial Reviews

Doody's Review Service
Reviewer: James C. Torner, PhD, MS (University of Iowa College of Public Health)
Description: This contemporary compilation of chapters on the main areas of biomedical research provides an overview of the state of analytical methods currently used and offers insights on future methods.
Purpose: This book is designed to highlight major contributions to methodology in topical areas, providing insight into methods and methodological issues. It does not provide mathematical details, which is not necessary given that the chapters are well referenced. It provides an excellent overview of contemporary statistical methods, meeting the authors' objectives of providing a compilation of key methods in biomedical science research.
Audience: This book is intended for advanced students or researchers. A basic understanding of common statistical methods is needed. The chapters are organized by subject matter so readers can find the methodology appropriate to their area. The authors for individual chapters are recognized contributors to the field.
Features: Areas covered include clinical trial design, epidemiology modeling, survival analysis, genetic analysis, bioinformatics, and other modeling topics. The book also focuses on contemporary methods. The outstanding organization and coverage is the best part of the book. The inconsistent use of examples is a shortcoming.
Assessment: This will be one of the landmark books that define the state of methods. It will provide an introduction and reference for advanced methods and will be a launching pad for future method development. It is challenging for the reader and may take several attempts and literature searches to use the methods, but the book lays the foundation. This is an impressive collection of topical methods.
From the Publisher
"Statistical Advances in the Biomedical Sciences providesvital statistical guidance to practioners in the biomedicalsciences while also introducing statisticians to new,multidisciplinary frontiers of application. This text is anexcellent reference for graduate - and Ph.D.-level courses invarious areas of biostatistics and the medical sciences and alsoserves as a valuable tool for medical researchers, statisticians,public health professionals, and biostatisticians."(Mathematical Reviews, Issue 2009f)

"Statistical Advances in the Biomedical Sciences provides vitalstatistical guidance to practioners in the biomedical scienceswhile also introducing statisticians to new, multidisciplinaryfrontiers of application.  This text is an excellent referencefor graduate - and Ph.D.-level courses in various areas ofbiostatistics and the medical sciences and also serves as avaluable tool for medical researchers, statisticians, public healthprofessionals, and biostatisticians." (Mathematical Reviews,Issue 2009f)

"The authors have done an excellent job of meeting the objectivethey put forward in the preface.  They have produced anauthoritative volume of readable chapters … The chapters arewritten well and will be understandable to graduate students inbiostatistics and statistics.  The book will have an importantplace as a reference book on the shelf of many professionalbiostatisticians working in a biomedical researchenvironment.  Additionally, it should be useful as a specialtopics text for graduate students in biostatistics and statisticsgraduate programs." (Biometrics, Dec 2008)

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Product Details

Meet the Author

Atanu Biswas, PhD, is Assistant Professor in the AppliedStatistics Unit at the Indian Statistical Institute, Kolkata inIndia. Dr. Biswas has authored more than eighty published articlesand also serves as Associate Editor of several journals, includingSequential Analysis and Communications in Statistics. He is therecipient of the M.N. Murthy Award for his research in appliedstatistics. Sujay Datta, PhD, is Associate Professor in theDepartment of Mathematics and Computer Science at Northern MichiganUniversity and Visiting Research Scientist in the Department ofStatistics at TexasA&M University, where he is part of abioinformatics research program sponsored by the NationalInstitutes of Health. Dr. Datta's research interests includehigh-throughput data, genomics, and models based ongraphs/networks. Jason P. Fine, PhD, is Associate Professor in theDepartment of Statistics at the University of Wisconsin-Madison andalso serves as Associate Editor of several journals, includingBiometrics, Biostatistics, and the Scandinavian Journal ofStatistics. Mark R. Segal, PhD, is Professor in the Department ofEpidemiology and Biostatistics at the University of California, SanFrancisco. A Fellow of the American Statistical Association, Dr.Segal has published extensively and currently focuses his researchin the area of bioinformatics.

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Table of Contents


1. Phase I Clinical Trials in Oncology (Anastasia Ivanova andNancy Flournoy).

1.1 Introduction.

1.2 Phase I Trials in Healthy Volunteers.

1.3 Phase I Trials With Toxic Outcomes Enrolling Patients.

1.4 Other Design Problems in Dose Finding.

1.5 Concluding Remarks.


2. Phase II Clinical Trials (Nigel Stallard).

2.1 Introduction.

2.2 Frequentist methods in phase II clinical trials.

2.3 Bayesian methods in phase II clinical trials.

2.4 Decision theoretic methods in phase II clinical trials.

2.5 Clinical trials combining phases II and III.

2.6 Outstanding issues in phase II clinical trials.


3. Response Adaptive Designs in Phase III Clinical Trials(Atanu Biswas, Uttam Bandyopadhyay and Rahul Bhattacharya).

3.1 Introduction

3.3 Adaptive Designs for Binary Treatment ResponsesIncorporating Covariates.

3.4 Adaptive Designs for Categorical Responses.

3.5 Adaptive Designs for Continuous Responses.

3.6 Optimal Adaptive Designs.

3.7 Delayed Responses in Adaptive Designs.

3.8 Biased Coin Designs.

3.9 Real Adaptive Clinical Trials.

3.10 Data Study for Different Adaptive Scheme.

3.11 Concluding Remarks.


4. Inverse Sampling for Clinical Trials: A Brief Review ofTheory and Practice (Atanu Biswas and Uttam Bandyopadhyay).

4.1 Introduction.

4.2 Two-Sample Randomized Inverse Sampling for ClinicalTrials.

4.3 An Example of Inverse Sampling: Boston ECMO.

4.4 Inverse Sampling in Adaptive Designs.

4.5 Concluding.

5. The Design and Analysis Aspects of Cluster RandomizedTrials (Hrishikesh Chakraborty).

5.1 Introduction: Cluster Randomized Trials.

5.2 Intra-Cluster Correlation Coefficient and ConfidenceInterval.

5.3 Sample Size Calculation for Cluster Randomized Trials.

5.4 Analysis of Cluster Randomized Trial Data.

5.5 Concluding Remarks.



6. HIV Dynamics Modeling and Prediction of Clinical Outcomesin AIDS Clinical Research (Yangxin Huang and Hulin Wu).

6.1 Introduction.

6.2 HIV Dynamic Model and Treatment Effects Models.

6.3 Statistical Methods for Predictions of ClinicalOutcomes.

6.4 Simulation Study.

6.5 Clinical Data Analysis.

6.6 Concluding Remarks.


7. Spatial Epidemiology (Lance A. Waller).

7.1 Space and Disease.

7.2 Basic Spatial Questions and Related Data.

7.3 Quantifying Pattern in Point Data.

7.4 Predicting Spatial Observations.

7.5 Concluding Remarks.


8. Modeling Disease Dynamics: Cholera as a Case Study (EdwardL. Ionides, Carles Breto and Aaron A. King).

8.1 Introduction.

8.2 Data Analysis via Population Models.

8.3 Sequential Monte Carlo.

8.4 Modeling Cholera.

8.5 Concluding Remarks.


9. Misclassification and Measurement Error Models inEpidemiological Studies (Surupa Roy and TathagataBanerjee).

9.1 Introduction.

9.2 A Few Examples.

9.3 Binary Regression Models with Two Types of Errors.

9.4 Bivariate Binary Regression Models with Two Types ofErrors.

9.5 Models for Analyzing Mixed Misclassified Binary andContinuous Responses.

9.6 Atom Bomb Data Analysis.

9.7 Concluding Remarks.



10. Semiparametric Maximum Likelihood Inference in SurvivalAnalysis (Michael R. Kosorok).

10.1 Introduction

10.2 Examples of Survival Models.

10.3 Basic Estimation and Limit Theory.

10.4 The Bootstrap.

10.5 The Profile Sampler.

10.6 The Piggyback Bootstrap.

10.7 Other Approaches.

10.8 Concluding Remarks.


11. An Overview of the Semi-Competing Risks Problem (LiminPeng, Hongyu Jiang, Richard J. Chappell and Jason P. Fine).

11.1 Introduction.

11.2 Nonparametric Inferences.

11.3 Semiparmetric One-Sample Inference.

11.4 Semiparametric Regression Method.

11.5 Concluding Remarks.


12. Tests for Time-Varying Covariate Effects within Aalen'sAdditive Hazards Model (Thomas H. Scheike and TorbenMartinussen).

12.1 Introduction.

12.2 Model Specification and Inferential Procedures.

12.3 Numerical Results.

12.4 Concluding Remarks.

12.5 Summary.


13. Analysis of Outcomes Subject to Induced DependentCensoring: A Marked Point Process Perspective (EugeneHuang).

13.1 Introduction.

13.2 Induced Dependent Censoring and Associated IdentifiabilityIssues.

13.3 Marked Point Process.

13.4 Modeling Strategy for Testing and Regression.

13.5 Concluding Remarks.


14. Analysis of Dependence in Multivariate Failure-Time Data(Zoe Moodie and Li Hsu).

14.1 Introduction.

14.2 Nonparametric Bivariate Survivor Function Estimation.

14.3 Non- and Semi-Parametric Estimation of DependenceMeasures.

14.4 Concluding Remarks.


15. Robust Estimation for Analyzing Recurrent Events Data inthe Presence of Terminal Events (Rajeshwari Sundaram).

15.1 Introduction.

15.2 Inference Procedures.

15.3 Large Sample Properties.

15.4 Numerical Results.

15.5 Concluding Remarks.


16. Tree-Based Methods for Survival Data (Mousumi Banerjeeand Anne-Michelle Noone).

16.1 Introduction.

16.2 Review of CART.

16.3 Trees for Survival Data.

16.4 Simulations to Compare Different Splitting Methods.

16.5 Example: Breast Cancer Prognostic Study.

16.6 Random forest for Survival Data.

16.7 Concluding Remarks.


17. Bayesian Estimation of the Hazard Function with RandomlyRight-Censored Data (Jean-Francois Angers and BrendaMacGibbon).

17.1 Introduction.

17.2 Bayesian Functional Model Using Monotone WaveletApproximation.

17.3 Estimation of the Sub-Density F*.

17.4 Simulations.

17.5 Example.

17.6 Concluding Remarks.



18. The Effects of Inter-Gene Associations on StatisticalInferences From Microarray Data (Kerby Shedden).

18.1 Introduction.

18.2 Inter-Gene Correlation.

18.3 Differential Expression.

18.4 Time Course Experiments.

18.5 Meta-Analysis.

18.6 Concluding Remarks.


19. A Comparison of Methods for Meta-Analysis of GeneExpression Data (Hyungwon Choi and Debashis Ghosh).

19.1 Introduction.

19.2 Background.

19.3 Example.

19.4 Cross Comparison of Gene Signatures.

19.5 Best Common Mean Difference Method.

19.6 Effect Size Method.

19.7 Probability of Expression (POE) Assimilation Method.

19.8 Comparison of Three Methods.

19.9 Conclusions.


20. Statistical Methods for Identifying DifferentiallyExpressed Genes in Replicated Microarray Experiments: A Review(Lynn Kuo, Fang Yu and Yifang Zhao).

20.1 Introduction.

20.2 Normalization.

20.3 Methods for Selecting Differentially Expressed Genes.

20.4 Simulation Study.

20.5 Concluding Remarks.


21. Clustering of Microarray Data via Mixture Models(Geoffrey McLachlan, Richard W. Bean and Angus Ng).

21.1 Introduction.

21.2 Clustering of Microarray Data.

21.3 Notation.

21.4 Clustering of Tissue Samples.

21.5 The EMMIX-GENE Clustering Procedure.

21.6 Clustering of gene profile.


21.8 ML Estimation via the EM Algorithm.

21.9 Model Selection.

21.10 Example: Clustering of Time-Course Data.

21.11 Concluding Remarks.


22. Censored Data Regression in High-Dimension andLow-Sample-Size Settings for Genomic Applications (HongzheLi).

22.1 Introduction.

22.2 Censored Data Regression Models.

22.3 Regularized Estimation for Censored Data RegressionModels.

22.4 Survival Ensemble Methods.

22.5 Nonparametric Pathway-Based Regression Models.

22.6 Dimension-Reduction-Based Methods and Bayesian VariableSelection Methods.

22.7 Criteria for Evaluating Different Procedures.

22.8 Application to a Real Data Set and Comparisons.

22.9 Discussion and Future Research Topics.

22.10 Concluding Remarks.


23. Analysis of Case-Control Studies in Genetic Epidemiology(Nilanjan Chatterjee).

23.1 Introduction.

23.2 Maximum Likelihood Analysis of Case-Control Data withComplete Information.

23.3 Haplotype-Based Genetic Analysis with Missing PhaseInformation

23.4 Concluding Remarks.


24. Assessing Network Structure in the Presence ofMeasurement Error (Denise Scholtens, Raji Balasubramanian andRobert Gentleman).

24.1 Introduction

24.2 Graphs of Biological Data.

24.3 Statistics on Graphs.

24.4 Graph Theoretic Models.

24.5 Types of Measurement Error.

24.6 Exploratory Data Analysis.

24.7 Influence of Measurement Error on Graph Statistics.

24.8 Biological Implications.

24.9 Conclusions.


25. Prediction of RNA Splicing Signals (Mark Segal).

25.1 Introduction.

25.2 Existing Approaches to Splice Site Identification.

25.3 Splice Site Recognition Contemporary Classifiers.

25.4 Results.

25.5 Concluding Remarks.


26. Statistical Methods for Biomarker Discovery Using MassSpectrometry (Bradley M. Broom and Kim-Anh Do).

26.1 Introduction.

26.2 Biomarker Discovery.

26.3 Statistical Methods for Pre-Processing.

26.4 Statistical Methods for Multiple Testing, Classificationand Applications spectra.

26.5 Potential Statistical Developments.

26.6 Concluding Remarks.


27. Genetic Mapping of Quantitative Traits: Model-FreeSib-Pair Linkage Approaches (Saurabh Ghosh and Parthe P.Majumder).

27.1 Introduction.

27.2 The Basic QTL Framework for Sib-Pairs.

27.3 The Haseman-Elston Regression Framework.

27.4 Nonparametric Alternatives.

27.5 The Modified Nonparametric Regression.

27.6 Comparison with Linear Regression Methods.

27.7 Significance Levels and Empirical Power.

27.8 An Application to Real Data.

27.9 Concluding Remarks.



28. Robustness Issues in Biomedical Studies (AyanendranathBasu).

28.1 Introduction: The Need for Robust Procedures.

28.2 Standard Tools for Robustness.

28.3 The Robustness Question in Biomedical Studies.

28.4 Robust Estimation in the Logistic Regression Model.

28.5 Robust Estimation for Censored Survival Data.

28.6 Adaptive Robust Methods in Clinical Trials.

28.7 Concluding Remarks.


29. Recent Advances in the Analysis of Episodic Hormone Data(Timothy D. Johnson and Yuedong Wang).

29.1 Introduction.

29.2 A General Biophysical Model.

29.3 Bayesian Deconvolution Model (BDM).

29.4 Nonlinear Mixed Effects Partial Splines Models.

29.5 Concluding Remarks.


30. Models for Carcinogenesis (Anup Dewanji).

30.1 Introduction.

30.2 Statistical Models.

30.3 Multistage Models.

30.4 Two-Stage Clonal Expansion Model.

30.5 Physiologically Based Pharmacokinetic Models.

30.6 Statistical Methods.

30.7 Concluding Remarks.


Author Index.

Subject Index.

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