Main description:

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.

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