Statistical Advances in the Biomedical

Composed of contributions from eminent researchers in the field, this book discusses the application of statistical techniques to various aspects of modern medical research and illustrates how these methods 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.

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.

Clinical Trials
Phase I Clinical Trials in Oncology
Introduction
Phase I Trials in Healthy Volunteers
Phase I Trials With Toxic Outcomes Enrolling Patients
Other Design Problems in Dose Finding
Concluding Remarks
References
Phase II Clinical Trials
Introduction
Frequentist methods in phase II clinical trials
Bayesian methods in phase II clinical trials
Decision theoretic methods in phase II clinical trials
Clinical trials combining phases II and III
Outstanding issues in phase II clinical trials
References
Response Adaptive Designs in Phase III Clinical Trials
Introduction
Adaptive Designs for Binary Treatment Responses Incorporating Covariates
Adaptive Designs for Categorical Responses
Adaptive Designs for Continuous Responses
Optimal Adaptive Designs
Delayed Responses in Adaptive Designs
Biased Coin Designs
Real Adaptive Clinical Trials
Data Study for Different Adaptive Scheme
Concluding Remarks
References
Inverse Sampling for Clinical Trials: A Brief Review of Theory and Practice
Introduction
Two-Sample Randomized Inverse Sampling for Clinical Trials
An Example of Inverse Sampling: Boston ECMO
Inverse Sampling in Adaptive Designs
Concluding
The Design and Analysis Aspects of Cluster Randomized Trials
Introduction: Cluster Randomized Trials
Intra-Cluster Correlation Coefficient and Confidence Interval
Sample Size Calculation for Cluster Randomized Trials
Analysis of Cluster Randomized Trial Data
Concluding Remarks
References
Epidemiology
HIV Dynamics Modeling and Prediction of Clinical Outcomes in AIDS Clinical Research
Introduction
HIV Dynamic Model and Treatment Effects Models
Statistical Methods for Predictions of Clinical Outcomes
Simulation Study
Clinical Data Analysis
Concluding Remarks
References
Spatial Epidemiology
Space and Disease
Basic Spatial Questions and Related Data
Quantifying Pattern in Point Data
Predicting Spatial Observations
Concluding Remarks
References
Modeling Disease Dynamics: Cholera as a Case Study
Introduction
Data Analysis via Population Models
Sequential Monte Carlo
Modeling Cholera
Concluding Remarks
References
Misclassification and Measurement Error Models in Epidemiological Studies
Introduction
A Few Examples
Binary Regression Models with Two Types of Errors
Bivariate Binary Regression Models with Two Types of Errors
Models for Analyzing Mixed Misclassified Binary and Continuous Responses
Atom Bomb Data Analysis
Concluding Remarks
References
Survival Analysis
Semiparametric Maximum Likelihood Inference in Survival Analysis
Introduction
Examples of Survival Models
Basic Estimation and Limit Theory
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Statistical Advances in the Biomedical Sciences Clinical Trials, Epidemiology, Survival Analysis, and Bioinformatics

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Table of Contents

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