Survival Analysis for Epidemiologic and Medical Research

This practical guide to survival data and its analysis for readers with a minimal background in statistics 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 as bias, confounding, independence, and interaction are presented in the context of survival analysis and also are basic components of a broad range of applications. These topics make up essentially a 'second-year', one-semester biostatistics course in survival analysis concepts and techniques for non-statisticians. '... for students who know very little about statistics and mathematics this book is a welcome resource on the techniques used to analyse time-to-event data. These techniques are introduced in a non-technical way accompanied with many good examples. Also the included R-code gives a good introduction to the practical analysis.' Biometrical Journal 'This book provides an easy-to-read introduction to the fundamental concepts applicable to survival analysis without relying on mathematical prerequisites. ... [the] text gives a thorough introduction to the area of survival analysis for those with little prior statistical knowledge.' International Journal of Epidemiology1. 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.