Survival & Failure Time Analysis Introductory Overview - General Information

Survival/Failure Time Analysis

The techniques available in this module were primarily developed in the medical and biological sciences, but they are also widely used in the social and economic sciences, as well as in engineering (see also Weibull and Reliability/Failure Time Analysis in the Process Analysis module).

Imagine that you are a researcher in a hospital who is studying the effectiveness of a new treatment for a generally terminal disease. The major variable of interest is the number of days that the respective patients survive. In principle, you could use the standard parametric and nonparametric statistics for describing the average survival, and for comparing the new treatment with traditional methods (see Basic Statistics and Nonparametrics Statistics). However, at the end of the study there will be patients who survived over the entire study period, in particular among those patients who entered the hospital (and the research project) late in the study; there will be other patients with whom we will have lost contact. Surely, you would not want to exclude all of those patients from the study by declaring them to be missing data (since most of them are "survivors" and, therefore, they reflect on the success of the new treatment method). Those observations, which contain only partial information are called censored observations (e.g., "patient A survived at least 4 months before he moved away and we lost contact;" the term censoring was first used by Hald, 1949).

See also, Censored Observations, Analytic Techniques, Life Table Analysis, Distribution Fitting, Kaplan-Meier Product-Limit Estimator, and Comparing Samples.