Survival modeling techniques are commonly used to extrapolate clinical trial outcomes like overall survival to a time horizon that is appropriate for health economic evaluations. Standard parametric distributions, such as the exponential and Weibull, have been the de-facto standard for conducting such extrapolations but with the advent of novel potentially curative therapies these standard parametric distributions fail to capture the underlying survival trend. Newer techniques like response‑based landmark models, parametric mixture models, mixture cure models and Bayesian model averaging provide novel ways to capture these more complex survival patterns. The purpose of this course is to enable participants to identify which methods are most appropriate in a specific context, considering underlying structural assumptions, and discuss how modeling choices propagate into health economic evaluations. To gain a more in-depth understanding of the impact of the choice for a specific method, participants will practice with several of the survival modeling techniques in hands-on exercises. The course assumes attendees have some survival modeling experience and basic understanding of Kaplan-Meier curves, standard survival modeling techniques, such as a Weibull distribution in a partitioned survival framework, and familiarity with coding in R. Participants who wish to gain hands-on experience are required to bring their laptops with R and RStudio installed.