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Survival analysis models factors that influence the time to an event. Ordinary least squares regression methods fall short because the time to event is typically not normally distributed, and the model cannot handle censoring, very common in survival data, without modification. Nonparametric methods provide simple and quick looks at the survival experience, and the Cox proportional hazards regression model remains the dominant analysis method. This seminar introduces procedures and outlines the coding needed in SAS to model survival data through both of these methods, as well as many techniques to evaluate and possibly improve the model. Particular emphasis is given to proc lifetest for nonparametric estimation, and proc phreg for Cox regression and model evaluation. Note: A number of sub-sections are titled Background. These provide some statistical background for survival analysis for the interested reader and for the author of the seminar! Provided the reader has some background in survival analysis, these sections are not necessary to understand how to run survival analysis in SAS.

A continuous probability distribution is a account of a variable that can abide a continuous range of values. A continuous probability distribution is a chance distribution that has a probability bulk function. There are many examples of continuous probability distributions: normal, uniform, chi-squared, and others. Intuitively, a continuous accidental variable is the one which be able to take a continuous range of values—as opposed to a discrete distribution, all the rage which the set of possible values for the random variable is by most countable.

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