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.
Contents
1. Rates and their properties; 2. Life tables; 3. Two especially useful estimation tools; 4. Product-limit estimation; 5. Exponential survival time probability distribution; 6. Weibull survival time probability distribution; 7. Analysis of two-sample survival data; 8. General hazards model: parametric; 9. General hazards model: nonparametric.
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