Reliability and Survival Methods > Survival Analysis > Overview of the Survival Analysis Platform
Publication date: 10/01/2019

Overview of the Survival Analysis Platform

Survival data need to be analyzed with specialized methods for two reasons:

1. The survival times usually have specialized non-normal distributions, like the exponential, Weibull, and lognormal.

2. Some of the data could be censored.

Survival functions are calculated using the nonparametric Kaplan-Meier method for one or more groups of either complete or right-censored data. Complete data have no censored values. Right-censoring is when you do not know the exact survival time, but you know that it is greater than the specified value. Right-censoring occurs when the study ends without all the units failing, or when a patient has to leave the study before it is finished. The censored observations cannot be ignored without biasing the analysis. The elements of a survival model are:

A time indicating how long until the unit (or patient) either experienced the event or was censored. Time is the model response (Y).

A censoring indicator that denotes whether an observation experienced the event or was censored. JMP uses the convention that the code for a censored unit is 1 and the code for a non-censored event is zero.

Explanatory variables (if a regression model is used.)

Interval censoring is when a data point is somewhere on an interval between two values. If interval censoring is needed, then two Y variables hold the lower and upper limits bounding the event time.

Common terms used for reliability and survival data include lifetime, life, survival, failure-time, time-to-event, and duration.

The Survival platform computes product-limit (Kaplan-Meier) survival estimates for one or more groups. It can be used as a complete analysis or is useful as an exploratory analysis to gain information for more complex model fitting. The Kaplan-Meier Survival platform does the following:

Shows a plot of the estimated survival function for each group. A plot for the whole sample is optional.

Calculates and lists survival function estimates for each group and for the combined sample.

Shows exponential, Weibull, and lognormal diagnostic failure plots to graphically check the appropriateness of using these distributions for further regression modeling. Parameter estimates are available on request.

Computes the Log Rank and generalized Wilcoxon Chi-square statistics to test homogeneity of the estimated survival function across groups.

Analyzes competing causes, prompting for a cause of failure variable, and estimating a Weibull failure time distribution for censoring patterns corresponding to each cause.

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