Overview

In this innovative new book, Steve Selvin provides readers with a clear understanding of intermediate biostatistical methods without advanced mathematics or statistical theory (for example, no Bayesian statistics, no causal inference, no linear algebra and only a slight hint of calculus). This text answers the important question: After a typical first-year course in statistical methods, what next?Statistical Tools for Epidemiologic Research thoroughly explains not just how statistical data analysis works, but how the analysis is accomplished. From the basic foundation laid in the introduction, chapters gradually increase in sophistication with particular emphasis on regression techniques (logistic, Poisson, conditional logistic and log-linear) and then beyond to useful techniques that are not typically discussed in an applied context. Intuitive explanations richly supported with numerous examples produce an accessible presentation for readers interested in the analysis of data relevant to epidemiologic or medical research.

Full Product Details

Author:   Steve Selvin (Professor of Biostatistics and Epidemiology, Professor of Biostatistics and Epidemiology, University of California, Berkeley)
Publisher:   Oxford University Press Inc
Imprint:   Oxford University Press Inc
Dimensions:   Width: 23.90cm , Height: 4.10cm , Length: 16.50cm
Weight:   0.885kg
ISBN:  

9780199755967

ISBN 10:   0199755965
Pages:   512
Publication Date:   03 February 2011
Audience:   College/higher education ,  Professional and scholarly ,  Tertiary & Higher Education ,  Professional & Vocational
Format:   Hardback
Publisher's Status:   Active
Availability:   To order  
Stock availability from the supplier is unknown. We will order it for you and ship this item to you once it is received by us.

Table of Contents

CHAPTER 1: Two measures of risk: odds ratios and average rates CHAPTER 2: Tabular data: the 2x k table and summarizing 2 x 2 tables CHAPTER 3: Two especially useful estimation tools CHAPTER 4: Linear logistic regression: discrete data CHAPTER 5: Logistic regression: continuous data CHAPTER 6: Analysis of count data: Poisson regression model CHAPTER 7: Analysis of matched case/control data CHAPTER 8: Spatial data: estimation and analysis CHAPTER 9: Classification: three examples CHAPTER 10: Three smoothing techniques CHAPTER 11: Case study: description and analysis CHAPTER 12: Longitudinal data analysis CHAPTER 13: Analysis of multivariate tables CHAPTER 14: Misclassification: a detailed description of a simple case CHAPTER 15: Advanced topics

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Author Information

Steve Selvin, PhD, is Professor and Head of Biostatistics at the School of Public Health, University of California, Berkeley.

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