Description
Book SynopsisThis very informative book introduces classical and novel statistical methods that can be used by theoretical and applied biostatisticians to develop efficient solutions for real-world problems encountered in clinical trials and epidemiological studies. The authors provide a detailed discussion of methodological and applied issues in parametric, semi-parametric and nonparametric approaches, including computationally extensive data-driven techniques, such as empirical likelihood, sequential procedures, and bootstrap methods. Many of these techniques are implemented using popular software such as R and SAS.â Vlad Dragalin, Professor, Johnson and Johnson, Spring House, PA
It is always a pleasure to come across a new book that covers nearly all facets of a branch of science one thought was so broad, so diverse, and so dynamic that no single book could possibly hope to capture all of the fundamentals as well as directions of the field. The topics within the bookâs purviewâfundamen
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"This very informative book introduces classical and novel statistical methods that can be used by theoretical and applied biostatisticians to develop efficient solutions for real-world problems encountered in clinical trials and epidemiological studies. The authors provide a detailed discussion of methodological and applied issues in parametric, semi-parametric and nonparametric approaches, including computationally extensive data-driven techniques, such as empirical likelihood, sequential procedures, and bootstrap methods. Many of these techniques are implemented using popular software such as R and SAS. "
—Vlad Dragalin, Vice President and Scientific Fellow, Quantitative Sciences, Johnson and Johnson
"It is always a pleasure to come across a new book that covers nearly all facets of a branch of science one thought was so broad, so diverse, and so dynamic that no single book could possibly hope to capture all of the fundamentals as well as directions of the field. Biostatistics is just such a branch of science and Statistics in the Health Sciences: Theory, Applications, and Computing is just such a book. Written by "lions" of the field, the book is an excellent piece of work that establishes an important bridge between Biostatistics and its numerous interfaces. The topics within the book’s purview—fundamentals of measure-theoretic probability; parametric and non-parametric statistical inference; central limit theorems; basics of martingale theory; Monte Carlo methods; sequential analysis; sequential change-point detection—are all covered with inspiring clarity and precision. The authors are also very thorough and avail themselves of the most recent scholarship. They provide a detailed account of the state of the art, and bring together results that were previously scattered across disparate disciplines. This makes the book more than just a textbook: it is a panoramic companion to the field of Biostatistics. The book is self-contained, and the concise but careful exposition of material makes it accessible to a wide audience. The book also offers numerous problems and computer projects, including data processing exercises, which range in complexity and degree of sophistication from introductory to fairly advanced. This is appealing to graduate students interested in getting into the field, and also to professors looking to design a course on the subject. To sum up, the book is a valuable addition to the literature, and certainly deserves a spot in the library. Perhaps the best way to express one’s gratitude to the authors would be to read the book."
—Aleksey S. Polunchenko, Ph.D., Department of Mathematical Sciences, State University of New York at Binghamton
Table of ContentsPrelude: Preliminary Tools and Foundations
Characteristic Function Based Inference
Likelihood Tenet
Martingale Type Statistics and Their Applications
Bayes Factor
A Brief Review of Sequential Methods
A Brief Review of Receiver Operating Characteristic Curve Analyses
The Ville and Wald Inequality: Extensions and Applications
Brief Comments on Confidence Intervals and P-Values
Empirical Likelihood
Jackknife and Bootstrap Methods
Examples of Homework Questions
Examples of Exams
Examples of Courses Projects
