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.

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.

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.