This versatile textbook allows students and teachers to fashion an instructional package that meets diverse learning needs. It provides a wide-ranging look at basic and advanced biostatistical concepts and methods in a format calibrated to individual interests and levels of proficiency. Each topic presentation features introductory comments, real-life examples, a step-by-step outline of the statistical procedure under discussion, an explanation of applications, and numerous practice exercises. Advanced material-which may be included in coursework at the discretion of the instructor-has been noted throughout the text with asterisks, and notes at the end of each chapter extend and enrich the primary material. Early chapters discuss the design of medical studies, descriptive statistics, and introductory ideas of probability theory and statistical inference. Later chapters explore more advanced statistical methods and illustrate important current uses of biostatistics. Statistical methods discussed include * Robustness and nonparametric statistics * Analysis of variance and covariance * Multiple comparisons * Discrimination and classification * Principal component analysis and factor analysis * Survival analysis (including life tables, product-limit estimates, and Cox proportional hazards regression) * Sample sizes for observational studies With more than 390 practice exercises, clear illustrations and graphics, and more than 130 examples, Biostatistics provides a complete detailed seminar, which encourages steady, incremental growth while acting as a catalyst for creative analysis.
This text is a great reference for everything from basic analysis to more advanced topics like longitudinal and time-to-event data. The book is well-written with relevant, current examples. If I had to choose one reference book, this would be it because of its depth and breadth of content (at the intermediate level).
nice intermediate level text in biostatistics
Published by Thriftbooks.com User , 24 years ago
The authors write well and cover most of the important topics very thoroughly. They motivate the subject very well with a number of important and "real world" examples in the first chapter.A unique feature is its detailed coverage of sample size determination in a number of contexts.The book was published in 1993 which is not recent enough to cover advances in meta analysis, resampling, Bayesian Hierarchical Models (with Markov Chain Monte Carlo Methods) and frailty models. At least bootstrap methods and meta analyses are mentioned in the book.Noteworthy are the full chapters on multiple comparison problems and discriminant analysis. This is an excellent reference book for biostatisticians.
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