Survival analysis is an analytical method focusing on time-to-event data. Frequently the event is death (overall survival), but many other events can be considered in this way, such as disease progression/relapse, or event occurrence (for prevention). Survival analysis is typically used in oncology, where patient survival (death from any cause) and time-to-progression are often key endpoints of a clinical trial: the analysis frequently forms the basis of associated economic evaluations using (partitioned) survival models. The attraction of survival analysis for economic evaluation is that economic endpoints such as (gains in) life-years and quality-adjusted life years are represented as areas under the (quality-adjusted) survival curve. Health outcomes are considered longitudinally over time, and not cross-sectionally at a specific point in time. The Kaplan-Meier method provides a non-parametric representation of survival over the time period that data was collected, allowing for incomplete patient records where patients are lost to follow up (censoring). Parametric representations of survival, defined using a number of different statistical distributions, such as Weibull, Gompertz or exponential, allow for survival to be extrapolated beyond measured patient experience, important for economic modelling.


How to cite: Survival Analysis [online]. (2016). York; York Health Economics Consortium; 2016.


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