- Membership
- MAA Press
- Meetings
- Competitions
- Community
- Programs
- Students
- High School Teachers
- Faculty and Departments
- Underrepresented Groups
- MAA Awards
- MAA Grants

- News
- About MAA

Publisher:

John Wiley

Publication Date:

2008

Number of Pages:

582

Format:

Hardcover

Series:

Wiley Series in Probability and Statistics

Price:

120.00

ISBN:

9780471947530

Category:

Anthology

[Reviewed by , on ]

Sarah Boslaugh

03/29/2008

*Statistical Advances in the Biomedical Sciences* is a collection of 30 peer-reviewed essays concentrating on four areas of biomedical research and applications, with an emphasis on recent advances within each area: clinical trials, epidemiology, survival analysis and bioinformatics. A few essays on miscellaneous topics are also included, which cover issues of robustness on biomedical studies, analysis of episodic hormonal data, and models for carcinogenesis.

This volume is not intended to be an exhaustive survey of contemporary biostatistics, but to provide an overview of some of the most important recent developments in the field. Each essay provides a “snapshot” of a particular technique or issue within biostatistics, understandable to laymen as well as specialists, going into some depth without the expectation that the area in question will be exhaustively covered. All chapters share a common structure: an introduction which discusses the general problem in context, followed by details about concepts, methods and algorithms;, and ending with a conclusion which summarizes the chapter and points to directions for future research.

The chapters may be read independently and the primary use of this volume is as a reference tool: if a researcher or graduate students needs to learn about semi-competing risks or spatial epidemiology, for instance, the chapters on those subjects will serve as easily-understood introductions to, and explications of, the topics in question. There is no bibliography, but each chapter is heavily footnoted, so the references section may act as a bibliography for those who wish to pursue the topic further. Even readers with minimal statistical background (for instance, medical journalists) will be able to understand something of the issues involved in many of the chapters simply by studying the introduction and conclusion and skimming the rest.

Atanu Biswas is an Associate Professor in the Applied Statistics Unit of the Indian Statistical Institute in Calcutta, India. Sujay Datta is a biostatistician in the Department of Statistics at Texas A & M University. Jason P. Fine is a professor in the departments of Biostatistics and Medical Informatics and Statistics at the University of Wisconsin, Madison. Mark. R. Segal is a Professor in the Division of Biostatistics and Director of the Center of Bioinformatics and Molecular Biostatistics at the University of California at San Francisco.

Sarah Boslaugh (seb5632@bjc.org) is a Performance Review Analyst for BJC HealthCare and an Adjunct Instructor in the Washington University School of Medicine, both in St. Louis, MO. Her books include *An Intermediate Guide to SPSS Programming: Using Syntax for Data Management* (Sage, 2004), *Secondary Data Sources for Public Health: A Practical Guide* (Cambridge, 2007), and *Statistics in a Nutshell* (O'Reilly, forthcoming), and she is Editor-in-Chief of *The Encyclopedia of Epidemiology* (Sage, forthcoming).

**1. Phase I Clinical Trials in Oncology (Anastasia Ivanova and Nancy 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.

References.

**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.

References.

**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 Responses Incorporating 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.

References.

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

4.1 Introduction.

4.2 Two-Sample Randomized Inverse Sampling for Clinical Trials.

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 Randomized Trials (Hrishikesh Chakraborty).**

5.1 Introduction: Cluster Randomized Trials.

5.2 Intra-Cluster Correlation Coefficient and Confidence Interval.

5.3 Sample Size Calculation for Cluster Randomized Trials.

5.4 Analysis of Cluster Randomized Trial Data.

5.5 Concluding Remarks.

References.

**SECTION II. EPIDEMIOLOGY.**

**6. HIV Dynamics Modeling and Prediction of Clinical Outcomes in 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 Clinical Outcomes.

6.4 Simulation Study.

6.5 Clinical Data Analysis.

6.6 Concluding Remarks.

References.

**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.

References.

**8. Modeling Disease Dynamics: Cholera as a Case Study (Edward L. 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.

References.

**9. Misclassification and Measurement Error Models in Epidemiological Studies (Surupa Roy and Tathagata Banerjee).**

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 of Errors.

9.5 Models for Analyzing Mixed Misclassified Binary and Continuous Responses.

9.6 Atom Bomb Data Analysis.

9.7 Concluding Remarks.

References.

**SECTION III. SURVIVAL ANALYSIS.**

**10. Semiparametric Maximum Likelihood Inference in Survival Analysis (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.

References.

**11. An Overview of the Semi-Competing Risks Problem (Limin Peng, 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.

References.

**12. Tests for Time-Varying Covariate Effects within Aalen's Additive Hazards Model (Thomas H. Scheike and Torben Martinussen).**

12.1 Introduction.

12.2 Model Specification and Inferential Procedures.

12.3 Numerical Results.

12.4 Concluding Remarks.

12.5 Summary.

References.

**13. Analysis of Outcomes Subject to Induced Dependent Censoring: A Marked Point Process Perspective (Eugene Huang).**

13.1 Introduction.

13.2 Induced Dependent Censoring and Associated Identifiability Issues.

13.3 Marked Point Process.

13.4 Modeling Strategy for Testing and Regression.

13.5 Concluding Remarks.

References.

**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 Dependence Measures.

14.4 Concluding Remarks.

References.

**15. Robust Estimation for Analyzing Recurrent Events Data in the 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.

References.

**16. Tree-Based Methods for Survival Data (Mousumi Banerjee and 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.

References.

**17. Bayesian Estimation of the Hazard Function with Randomly Right-Censored Data (Jean-Francois Angers and Brenda MacGibbon).**

17.1 Introduction.

17.2 Bayesian Functional Model Using Monotone Wavelet Approximation.

17.3 Estimation of the Sub-Density F*.

17.4 Simulations.

17.5 Example.

17.6 Concluding Remarks.

References.

**SECTION IV. GENOMICS AND PROTEOMICS.**

**18. The Effects of Inter-Gene Associations on Statistical Inferences 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.

References.

**19. A Comparison of Methods for Meta-Analysis of Gene Expression 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.

References.

**20. Statistical Methods for Identifying Differentially Expressed 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.

References.

**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.

21.7 EMMIX-WIRE.

21.8 ML Estimation via the EM Algorithm.

21.9 Model Selection.

21.10 Example: Clustering of Time-Course Data.

21.11 Concluding Remarks.

References.

**22. Censored Data Regression in High-Dimension and Low-Sample-Size Settings for Genomic Applications (Hongzhe Li).**

22.1 Introduction.

22.2 Censored Data Regression Models.

22.3 Regularized Estimation for Censored Data Regression Models.

22.4 Survival Ensemble Methods.

22.5 Nonparametric Pathway-Based Regression Models.

22.6 Dimension-Reduction-Based Methods and Bayesian Variable Selection 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.

References.

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

23.1 Introduction.

23.2 Maximum Likelihood Analysis of Case-Control Data with Complete Information.

23.3 Haplotype-Based Genetic Analysis with Missing Phase Information

23.4 Concluding Remarks.

References.

**24. Assessing Network Structure in the Presence of Measurement Error (Denise Scholtens, Raji Balasubramanian and Robert 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.

References.

**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.

References.

**26. Statistical Methods for Biomarker Discovery Using Mass Spectrometry (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, Classification and Applications spectra.

26.5 Potential Statistical Developments.

26.6 Concluding Remarks.

References.

**27. Genetic Mapping of Quantitative Traits: Model-Free Sib-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.

References.

**SECTION V. MISCELLANEOUS TOPICS.**

**28. Robustness Issues in Biomedical Studies (Ayanendranath Basu).**

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.

References.

**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.

References.

**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.

References.

Author Index.

Subject Index.

- Log in to post comments