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Basic and Clinical Biostatistics by A Doody's Core Title for 2015! A comprehensive user-friendly introduction to biostatistics and epidemiology applied to medicine, clinical practice, and research. Features "Presenting Problems" (case studies) drawn from studies published in the medical literature, end-of-chapter, and a CD-ROM with data sets and statistical software programs.
Publication Date: 2004-04-15
Biostatistics and Epidemiology by Since the publication of the first edition, Biostatistics and Epidemiology has attracted loyal readers from across specialty areas in the biomedical community. Not only does this textbook teach foundations of epidemiological design and statistical methods, but it also includes topics applicable to new areas of research. Areas covered in the fourth edition include a new chapter on risk prediction, risk reclassification and evaluation of biomarkers, new material on propensity analyses, and a vastly expanded chapter on genetic epidemiology, which is particularly relevant to those who wish to understand the epidemiological and statistical aspects of scientific articles in this rapidly advancing field. Biostatistics and Epidemiology was written to be accessible for readers without backgrounds in mathematics. It provides clear explanations of underlying principles, as well as practical guidelines of "how to do it" and "how to interpret it." Key features include a philosophical and logical explanation at the beginning of the book, subsections that can stand alone or serve as reference, cross-referencing, recommended reading, and appendices covering sample calculations for various statistics in the text.
Publication Date: 2015-02-06
Regression Methods in Biostatistics by This new book provides a unified, in-depth, readable introduction to the multipredictor regression methods most widely used in biostatistics: linear models for continuous outcomes, logistic models for binary outcomes, the Cox model for right-censored survival times, repeated-measures models for longitudinal and hierarchical outcomes, and generalized linear models for counts and other outcomes. Treating these topics together takes advantage of all they have in common. The authors point out the many-shared elements in the methods they present for selecting, estimating, checking, and interpreting each of these models. They also show that these regression methods deal with confounding, mediation, and interaction of causal effects in essentially the same way. The examples, analyzed using Stata, are drawn from the biomedical context but generalize to other areas of application. While a first course in statistics is assumed, a chapter reviewing basic statistical methods is included. Some advanced topics are covered but the presentation remains intuitive. A brief introduction to regression analysis of complex surveys and notes for further reading are provided.
Publication Date: 2012-03-06
Statistical Methods for Biostatistics and Related Fields by This book covers a wide range of recent statistical methods that are of interest to scientists in biostatistics as well as in other related fields such as chemometrics, environmetrics and geophysics. The contributed papers, from internationally recognized researchers, present various statistical methodologies together with a selected scope of their main mathematical properties and their application in a real case study.
Publication Date: 2006-11-24