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Modelling of relative treatment effects is an important aspectto consider when extrapolating the long-term survival outcomes of treatments.Flexible parametric models offer the ability to accurately model the observeddata, however, the extrapolated relative treatment effects and subsequentsurvival function may lack face validity. Methods: We investigate the abilityof change-point survival models to estimate changes in the relative treatmenteffects, specifically treatment delay, loss of treatment effects and converginghazards. These models are implemented using standard Bayesian statisticalsoftware and propagate the uncertainty associate with all model parametersincluding the change-point location. A simulation study was conducted to assessthe predictive performance of these models compared with other parametricsurvival models. Change-point survival models were applied to three datasets,two of which were used in previous health technology assessments. Results:Change-point survival models typically provided improved extrapolated survivalpredictions, particularly when the changes in relative treatment effects arelarge. When applied to the real world examples they provided good fit to theobserved data while and in some situations produced more clinically plausibleextrapolations than those generated by flexible spline models. Change-pointmodels also provided support to a previously implemented modelling approachwhich was justified by visual inspection only and not goodness of fit to theobserved data. Conclusions: We believe change-point survival models offer theability to flexibly model observed data while also modelling and investigatingclinically plausible scenarios with respect to the relative treatment effects.

Extrapolation of Relative Treatment Effects using Change-point Survival Models