Surv Analysis Epidemiologic Med Res

This practical guide shows why the analytic methods work and how to effectively analyze and interpret epidemiologic and medical survival data with the help of modern computer systems. The introduction presents a review of a variety of statistical methods that are not only key elements of survival analysis but are also central to statistical analysis in general. Techniques such as statistical tests transformations confidence intervals and analytic modeling are presented in the context of survival data but are in fact statistical tools that apply to understanding the analysis of many kinds of data. Similarly discussions of such statistical concepts such as bias confounding independence and interaction are presented in the context of survival analysis as well as the basic components of a broad range of applications.