Statistical Advances in the Biomedical Sciences

Clinical Trials, Epidemiology, Survival Analysis, and Bioinformatics
Herausgegeben:Biswas, Atanu; Datta, Sujay; Fine, Jason P.; Segal, Mark R.

• Gebundenes Buch

Produktbeschreibung

This book compiles 30 carefully prepared and peer-reviewed articles form eminent researchers in the modern-day biomedical sciences. With sections on Clinical Trials, Epidemiology, Survival Analysis, and Bioinformatics, it provides more specific details on concepts, methods, and algorithms.

Provides a wide view of contemporary methodological developments and technical innovations with a focus on applications. A related website offers datasets used or referred to in the book and suggests links to the commercial or freely downloadable software packages referred to in the various chapters.

---

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 sciencesand also serves as a valuable tool for medical researchers, statisticians, public health professionals, and biostatisticians.

Produktdetails

• Wiley Series in Probability and Statistics
• Verlag: Wiley & Sons
• Artikelnr. des Verlages: 14694753000
• 1. Auflage
• Seitenzahl: 616
• Erscheinungstermin: 2. Januar 2008
• Englisch
• Abmessung: 237mm x 163mm x 32mm
• Gewicht: 990g
• ISBN-13: 9780471947530
• ISBN-10: 0471947539
• Artikelnr.: 22863254

Autorenporträt

Atanu Biswas, PhD, is Assistant Professor in the Applied Statistics Unit at the Indian Statistical Institute, Kolkata in India. Dr. Biswas has authored more than eighty published articles and also serves as Associate Editor of several journals, including Sequential Analysis and Communications in Statistics. He is the recipient of the M.N. Murthy Award for his research in applied statistics. Sujay Datta, PhD, is Associate Professor in the Department of Mathematics and Computer Science at Northern Michigan University and Visiting Research Scientist in the Department of Statistics at TexasA&M University, where he is part of a bioinformatics research program sponsored by the National Institutes of Health. Dr. Datta's research interests include high-throughput data, genomics, and models based on graphs/networks. Jason P. Fine, PhD, is Associate Professor in the Department of Statistics at the University of Wisconsin-Madison and also serves as Associate Editor of several journals, including Biometrics, Biostatistics, and the Scandinavian Journal of Statistics. Mark R. Segal, PhD, is Professor in the Department of Epidemiology and Biostatistics at the University of California, San Francisco. A Fellow of the American Statistical Association, Dr. Segal has published extensively and currently focuses his research in the area of bioinformatics.

Inhaltsangabe

SECTION I.CLINICAL TRIALS. 1. Phase I Clinical Trials in Oncology
(Anastasia Ivanova and Nancy Flournoy). References. 2. Phase II Clinical
Trials (Nigel Stallard). References. 3. Response Adaptive Designs in Phase
III Clinical Trials (Atanu Biswas, Uttam Bandyopadhyay and Rahul
Bhattacharya). References. 4. Inverse Sampling for Clinical Trials: A Brief
Review of Theory and Practice (Atanu Biswas and Uttam Bandyopadhyay). 5.
The Design and Analysis Aspects of Cluster Randomized Trials (Hrishikesh
Chakraborty). References. SECTION II. EPIDEMIOLOGY. 6. HIV Dynamics
Modeling and Prediction of Clinical Outcomes in AIDS Clinical Research
(Yangxin Huang and Hulin Wu). References. 7. Spatial Epidemiology (Lance A.
Waller). References. 8. Modeling Disease Dynamics: Cholera as a Case Study
(Edward L. Ionides, Carles Breto and Aaron A. King). References. 9.
Misclassification and Measurement Error Models in Epidemiological Studies
(Surupa Roy and Tathagata Banerjee). References. SECTION III. SURVIVAL
ANALYSIS. 10. Semiparametric Maximum Likelihood Inference in Survival
Analysis (Michael R. Kosorok). References. 11. An Overview of the
Semi-Competing Risks Problem (Limin Peng, Hongyu Jiang, Richard J. Chappell
and Jason P. Fine). References. 12. Tests for Time-Varying Covariate
Effects within Aalen's Additive Hazards Model (Thomas H. Scheike and Torben
Martinussen). References. 13. Analysis of Outcomes Subject to Induced
Dependent Censoring: A Marked Point Process Perspective (Eugene Huang).
References. 14. Analysis of Dependence in Multivariate Failure-Time Data
(Zoe Moodie and Li Hsu). References. 15. Robust Estimation for Analyzing
Recurrent Events Data in the Presence of Terminal Events (Rajeshwari
Sundaram). References. 16. Tree-Based Methods for Survival Data (Mousumi
Banerjee and Anne-Michelle Noone). References. 17. Bayesian Estimation of
the Hazard Function with Randomly Right-Censored Data (Jean-Francois Angers
and Brenda MacGibbon). References. SECTION IV. GENOMICS AND PROTEOMICS. 18.
The Effects of Inter-Gene Associations on Statistical Inferences From
Microarray Data (Kerby Shedden). References. 19. A Comparison of Methods
for Meta-Analysis of Gene Expression Data (Hyungwon Choi and Debashis
Ghosh). References. 20. Statistical Methods for Identifying Differentially
Expressed Genes in Replicated Microarray Experiments: A Review (Lynn Kuo,
Fang Yu and Yifang Zhao). References. 21. Clustering of Microarray Data via
Mixture Models (Geoffrey McLachlan, Richard W. Bean and Angus Ng).
References. 22. Censored Data Regression in High-Dimension and
Low-Sample-Size Settings for Genomic Applications (Hongzhe Li). References.
23. Analysis of Case-Control Studies in Genetic Epidemiology (Nilanjan
Chatterjee). References. 24. Assessing Network Structure in the Presence of
Measurement Error (Denise Scholtens, Raji Balasubramanian and Robert
Gentleman). References. 25. Prediction of RNA Splicing Signals (Mark
Segal). References. 26. Statistical Methods for Biomarker Discovery Using
Mass Spectrometry (Bradley M. Broom and Kim-Anh Do). References. 27.
Genetic Mapping of Quantitative Traits: Model-Free Sib-Pair Linkage
Approaches (Saurabh Ghosh and Parthe P. Majumder). References. SECTION V.
MISCELLANEOUS TOPICS. 28. Robustness Issues in Biomedical Studies
(Ayanendranath Basu). References. 29. Recent Advances in the Analysis of
Episodic Hormone Data (Timothy D. Johnson and Yuedong Wang). References.
30. Models for Carcinogenesis (Anup Dewanji). References. Author Index.
Subject Index.

Rezensionen

"Statistical Advances in the Biomedical Sciences provides vital statistical guidance to practioners in the biomedical sciences while also introducing statisticians to new, multidisciplinary frontiers of application. This text is an excellent reference for graduate - and Ph.D.-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." ( Mathematical Reviews , Issue 2009f)
"The authors have done an excellent job of meeting the objective they put forward in the preface. They have produced an authoritative volume of readable chapters ... The chapters are written well and will be understandable to graduate students in biostatistics and statistics. The book will have an important place as a reference book on the shelf of many professional biostatisticians working in a biomedical research environment. Additionally, it should be useful as a special topics text for graduate students in biostatistics and statistics graduate programs." ( Biometrics, Dec 2008)