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Abstract:
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Survival analysis provides insights into time-to-event data. Well-established methods focus on right-censored data; these methods include the Kaplan-Meier method, the log-rank test, and the Cox proportional hazards regression model. Survival analysis techniques continue to evolve to meet new challenges, and the latest updates include advanced methods that deal with interval-censored data and competing risks data, as well as statistics for assessing survival models to facilitate risk prediction. This tutorial begins with a review of basic concepts and then presents two sets of model assessment methods-concordance statistics and time-dependent receiver-operating characteristic (ROC) curves. Next, the tutorial introduces analysis of interval-censored data, and how to estimate and compare survival functions with interval-censored data as well as perform proportional hazards regression. The tutorial then turns to analysis of competing risks data, including the use of nonparametric survival analysis and how to investigate the relationship of covariates to cause-specific failures. The cause-specific hazard regression approach will be discussed and compared to Fine and Gray's subdistribution approach. Applications are demonstrated with the survival procedures of recent SAS/STAT software releases.
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