Statistical Tools for Epidemiologic Research

Focuses on 'second-year' techniques, providing a clear understanding of intermediate biostatistical methods without advanced mathematics or statistical theory
Contains well-tested material that has been developed over the years for Selvin's popular graduate-level Epidemiologic Analysis course at UC Berkeley.

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.

Readership: Professors, students, and researchers in public health, epidemiology and/or biostatistics.