Extending Beyond Bagust and Beale: Fully Parametric Piecewise Exponential Models for Extrapolation of Survival Outcomes in Health Technology Assessment

When extrapolating time-to-event data the Bagust and Beale (B&B) approach uses the Kaplan-Meier survival function until a manually chosen time point, after which a constant hazard is assumed. This study demonstrates an objective statistical approach to estimate this time point. We estimate piecewise exponential models (PEMs), whereby the hazard function is partitioned into segments each with constant hazards. The boundaries of these segments are known as change points. Our approach determines the location and number of change points in PEMs from which the hazard in the final segment is used to model long-term survival. We reviewed previous applications of the B&B approach in National Institute for Health and Care Excellence Technology Appraisals (TAs) completed between July 2011 and June 2017. The time points after which constant hazards were assumed were compared between PEMs and the B&B approaches. When further survival data were published following the original TA, we compared these updated estimates to predicted survival from the PEM and other parametric models adjusted for general population mortality. Six of the 59 TAs in this review considered the B&B approach. There was general agreement between the location of time points identified through the PEM and the B&B approaches. In 2 of the identified TAs the best fitting model to the data was a no-change-point model. Of the 3 TAs for which further survival data became available, PEM provided the closest prediction for survival outcomes in 2 TAs. PEMs are useful for survival extrapolation when a long-term constant hazard trend for the disease is clinically plausible. •For clinical and administrative reasons, the early portion of clinical trials can be subject to transient effects that are not representative of the long-term hazards and can potentially bias survival extrapolation.•In this article, we describe a survival model that objectively identifies the location after which disease-related hazards become approximately constant and compare the accuracy of extrapolated survival against other parametric models.•This study illustrates that if disease-related hazards can be assumed constant, the extrapolated survival (adjusting for general population mortality) can be a reasonable estimate for use in decision-analytic model-based cost-effectiveness analysis.