The description of survival analysis techniques can be mathematically complex. The primary goal of the following description, however, is a sophisticated introduction to survival analysis theory and practice using only elementary mathematics, with an emphasis on examples and intuitive explanations. The mathematical level is completely accessible with knowledge of high school algebra, a tiny bit of calculus, and a one-year course in basic statistical methods (for example, t-tests, chi-square analysis, correlation, and some experience with linear regression models). With this minimal background, the reader will be able to appreciate why the analytic methods work and, with the help of modern computer systems, to effectively analyze and interpret much of epidemiologic and medical survival data.

A secondary goal is the introduction (perhaps the review) of a variety of statistical methods that are key elements of survival analysis but are also central to statistical data analysis in general. Such techniques as statistical tests, transformations, confidence intervals, analytic modeling, and likelihood methods are presented in the context of survival data but, in fact, are statistical tools that apply to many kinds of data. Similarly, discussions of such statistical concepts as bias, confounding, independence, and interaction are presented in the context of survival analysis but also are basic to a broad range of applications.

To achieve these two goals, the presented material is divided into nine topics:

1. Chapter 1: Rates and their properties

2. Chapter 2: Life tables

3. Chapter 3: Two especially useful estimation tools

4. Chapter 4: Product-limit estimation

5. Chapter 5: Exponential survival time probability distribution

6. Chapter 6: Weibull survival time probability distribution

7. Chapter 7: Analysis of two-sample survival data

8. […]