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Library | Item Barcode | Call Number | Material Type | Item Category 1 | Status |
|---|---|---|---|---|---|
Searching... | 30000010167826 | R853.S7 S72 2008 | Open Access Book | Book | Searching... |
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Summary
Summary
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.
Author Notes
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.
Table of Contents
| Section I Clinical Trials |
| 1 Phase I Clinical Trials in OncologyAnastasia 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 TrialsNigel 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 TrialsAtanu Biswas and 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 PracticeAtanu 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 TrialsHrishikesh 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 ResearchYangxin 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 EpidemiologyLance 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 StudyEdward L. Ionides and 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 StudiesSurupa 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 AnalysisMichael R. Kosorok |
| 10.1 Introduction |
| 10.2 Examples of Survival Models |
| 10.3 Basic Estimation and Limit Theory |
