Statistical Tools for Epidemiologic Research

Hardcover | January 28, 2011

bySteve Selvin

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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). Thistext 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 datarelevant to epidemiologic or medical research.

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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). Thistext answers the important question: After ...

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

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Format:HardcoverDimensions:448 pages, 9.25 × 6.12 × 0.98 inPublished:January 28, 2011Publisher:Oxford University PressLanguage:English

The following ISBNs are associated with this title:

ISBN - 10:0199755965

ISBN - 13:9780199755967

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Table of Contents

1. Two measures of risk: odds ratios and average rates2. Tabular data: the 2X k table and summarizing 2 X 2 tables3. Two especially useful estimation tools4. Linear logistic regression: discrete data5. Logistic regression: continuous data6. Analysis of count data: Poisson regression model7. Analysis of matched case/control data8. Spatial data: estimation and analysis9. Classification: three examples10. Three smoothing techniques11. Case study: description and analysis12. Longitudinal data analysis13. Analysis of multivariate tables14. Misclassification: a detailed description of a simple case15. Advanced topics