Epidemiologic

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?

The book begins with a public health/epidemiologic examination of the situation, with a systematic view of the problem based on classic (infectious disease) epidemiologic principles applied to this behavioral health issue.
This application is expanded to take into account the fact that unlike most epidemics in our species’ history, a unique factor that must be accounted for in any public health analysis is that the host (opioid-dependent/misusing patient) is generally not under sufficiently strong motivation to avoid the disease. The introduction concludes with a survey of recent containment approaches, categorized epidemiologically by target (agent, vector, host.)

This text provides the first-ever compilation of bias analysis methods for use with epidemiologic data. It guides the reader through the planning stages of bias analysis, including the design of validation studies and the collection of validity data from other sources. Three chapters present methods for corrections to address selection bias, uncontrolled confounding, and classification errors. Subsequent chapters extend these methods to multidimensional bias analysis, probabilistic bias analysis, and multiple bias analysis.