Statistics - Advanced Linear/Nonlinear Models - Survival Analysis

Ribbon bar. Select the Statistics tab. In the Advanced/Multivariate group, click Advanced Models and on the menu, select Survival to display the Survival and Failure Time Analysis Startup Panel.

Classic menus. On the Statistics - Advanced Linear/Nonlinear Models submenu, select Survival Analysis to display the Survival and Failure Time Analysis Startup Panel.

Survival analysis (exploratory and hypothesis testing) techniques include descriptive methods for estimating the distribution of survival times from a sample, methods for comparing survival in two or more groups, and techniques for fitting linear or nonlinear regression models to survival data. A defining characteristic of survival time data is that they usually include "censored observations," e.g., observations that "survived" to a certain point in time, and then dropped out from the study (patients who are discharged from a hospital). Instead of discarding such observations from the data analysis altogether (i.e., unnecessarily lose potentially useful information) survival analysis techniques can accommodate censored observations, and "use" them in statistical significance testing and model fitting.

The Survival Analysis module features a comprehensive implementation of a variety of techniques for analyzing censored data. In addition to computing life tables with various descriptive statistics and Kaplan-Meier product limit estimates, you can compare the survivorship functions in different groups using a large selection of methods (including the Gehan test, Cox F-test, Cox-Mantel test, Log-rank test, and Peto & Peto generalized Wilcoxon test). Also, Kaplan-Meier plots can be computed for groups (uncensored observations are identified in graphs with different point markers). STATISTICA also features a selection of survival function fitting procedures (including the Exponential, Linear Hazard, Gompertz, and Weibull functions) based on either unweighted and weighted least squares methods. STATISTICA also offers full implementations of four general explanatory models (Cox's proportional hazard model, exponential regression model, log-normal and normal regression models) with extended diagnostics, including stratified analyses and graphs of survival for user-specified values of predictors. For Cox proportional hazard regression, you can choose to stratify the sample to permit different baseline hazards in different strata (but a constant coefficient vector), or you can allow for different baseline hazards as well as coefficient vectors. In addition, general facilities are provided to define one or more time-dependent covariates.

For engineering applications, see also Weibull Analysis options in the Process Analysis module.