Statistics for Epidemiology Review

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(More customer reviews)I had a chance to read this book cover to cover. All I can say is "absolutely outstanding", short of calling it a historical masterpiece in the field. Very rarely do I encounter an epidemiology or biostatistic textbook that reads so well. It is optimally reader friendly; the author appears to have such a talent in explaining some most sophisticated epidemiological and statistical concepts in such a simplified language. Yet he does not sacrifice the inclusion of some very advanced epidemiological and statistical concepts. New concepts such as causal graphs and instrumental variables are also included and explained beautifully. I strongly recommend this book to all early to intermediate graduate students majoring in Epidemiology. Established epidemiologists may wish to read this book to refresh and update their knowledge. I hope the author writes more textbooks with the same style.

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Statistical ideas have been integral to the development of epidemiology and continue to provide the tools needed to interpret epidemiological studies. Although epidemiologists do not need a highly mathematical background in statistical theory to conduct and interpret such studies, they do need more than an encyclopedia of "recipes."Statistics for Epidemiology achieves just the right balance between the two approaches, building an intuitive understanding of the methods most important to practitioners and the skills to use them effectively. It develops the techniques for analyzing simple risk factors and disease data, with step-by-step extensions that include the use of binary regression. It covers the logistic regression model in detail and contrasts it with the Cox model for time-to-incidence data. The author uses a few simple case studies to guide readers from elementary analyses to more complex regression modeling. Following these examples through several chapters makes it easy to compare the interpretations that emerge from varying approaches.Written by one of the top biostatisticians in the field, Statistics for Epidemiology stands apart in its focus on interpretation and in the depth of understanding it provides. It lays the groundwork that all public health professionals, epidemiologists, and biostatisticians need to successfully design, conduct, and analyze epidemiological studies.

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Handbook of Statistics, Volume 27: Epidemiology and Medical Statistics Review

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(More customer reviews)This series is a long and successful collection of volumes on the most important topics in statistics. Professor Krishnaiah from the University of Pittsburgh was one of the first editors. When the famous Professor C. R. Rao moved from India to the US he joined his colleague Krishnaiah at Pittsburgh and helped coedit some of these handbooks. After Krishnaiah's death Professor Rao continued to edit these volumes and brought in some of his colleagues as co-editors.
This is volume 27 and it contains 27 chapters on the topic of medical statistics and epidemiology. This is very important to me as I work in the medical research area.
The editors have assembly the leading experts in statistical methodology and applied biostatistical reserach to produce these 27 chapters.
Ross Prentice (coauthor of 2 edition of a book on survival analysis and the Cox model) wrote an overview chapter on the topics to be covered. He has had a long career at the Fred Hutchinson Cancer Research Center in Seattle Washington. Fred Hutchinson was a famous manager for the Cincinnati Reds in the early 1960s and brought them to the World Series in 1961. He died of cancer and his estate started an endowment to form this research center.
Don Rubin covers causal analysis in Chapter 2. Rubin is an expert on causal inference, Bayesian methods and missing data. He presents the randomized trial approaches of Fisher and Neyman introduces propensity scores. A Bayesian approach is also covered.
Other chapters cover epidemiologic study designs, assessments for biomarkers, linear and nonlinear regression models, logistic regression, regression models for count data, mixed linear and nonlinear models, survival analysis, competing risk models, cluster analysis, factor analysis, structural equation models, crossover trials, group sequential methods, early phase trials (I and II), late phase (III and IV), missing data, Meta-Analysis, multiple comparisons, sample size determination
statistical learning using splines, evidence based medicine, marginal regression models, use of difference equations in modeling and the Bayesian approach to medical inference.
This volume is over 800 pages and each chapter is fairly long and includes large bibliographies.

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