Survival Analysis for Health Economics and Clinical Decision-Making
Summary
Survival analysis comprises statistical methods designed to estimate the time until an event of interest, typically death, disease progression or treatment failure. In health economics, these methods underpin cost-effectiveness models by projecting long-term outcomes and associated costs beyond the duration of clinical trials. Central techniques include non-parametric estimators such as the Kaplan–Meier curve, semi-parametric regression exemplified by the Cox proportional hazards model, and fully parametric approaches that assume a specific distribution for event times. Extrapolation of survival curves allows policy-makers to predict lifetime benefits and to inform reimbursement decisions, while measures such as restricted mean survival time offer robust alternatives when proportional hazards assumptions are violated. Recent advances have introduced flexible parametric spline functions, mixture cure models and multi-state frameworks, enhancing the capacity to capture complex disease dynamics, treatment switching and background mortality. These developments support nuanced clinical decision-making by quantifying uncertainty, integrating external real-world evidence and facilitating probabilistic sensitivity analyses. The global significance of these methods is reflected in guidelines from health technology assessment agencies, which require lifetime horizon analyses to ensure that the value of novel interventions is appraised against both clinical effectiveness and economic sustainability.
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Survival Analysis for Health Economics and Clinical Decision-Making publication trend
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Technical terms
Survival function: The probability of surviving beyond a given time point.
Hazard function: The instantaneous event rate at a given time, conditional on survival up to that time.
Censoring: Occurs when the event of interest is not observed for some subjects within the study period.
Proportional hazards assumption: The presumption that hazard ratios between groups remain constant over time.
Parametric survival model: A model that specifies a mathematical form (e.g. Weibull, exponential) for the survival distribution.
Restricted mean survival time: The average time to event truncated at a pre-specified time horizon.
Extrapolation: Extension of survival estimates beyond the observed follow-up, often by fitting parametric or spline models.
References
- SurvInt: a simple tool to obtain precise parametric survival extrapolations. BMC Medical Informatics and Decision Making (2024).
- Extrapolating Survival from Randomized Trials Using External Data: A Review of Methods. Medical Decision Making (2016).
- flexsurv: A Platform for Parametric Survival Modeling in R.. Journal of Statistical Software (2016).
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