Oct 1, 2023, 00:00 AM

10.1016/j.jval.2023.06.007

https://www.valueinhealthjournal.com/article/S1098-3015(23)03022-X/fulltext

When extrapolating time-to-event data the Bagust and Beale (BB) 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 BB 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 BB 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.

https://www.valueinhealthjournal.com/action/showCitFormats?pii=S1098-3015(23)03022-X&doi=10.1016/j.jval.2023.06.007

- Clinical Outcomes
- Decision Modeling & Simulation
- Methodological & Statistical Research
- Modeling and simulation
- Relating Intermediate to Long-term Outcomes
- Study Approaches

- “Bagust and Beale” approach
- Bayesian analysis
- change points
- constant hazard
- hazard function
- piecewise exponential

- Global