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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). 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.

About the Author

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

Table of Contents

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

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