Utilizing Expert Opinion to inform Extrapolation of Survival Models

TL;DR

This paper introduces a Bayesian and frequentist method to incorporate expert opinions into survival model extrapolation, reducing uncertainty in long-term survival estimates with moderate censoring.

Contribution

It presents a flexible, easy-to-implement approach for including expert opinions in survival analysis, addressing aggregation and validation issues.

Findings

Expert opinions can be integrated via penalized likelihood.

The method reduces model uncertainty in survival extrapolation.

Validation against existing approaches shows improved robustness.

Abstract

In decision modelling with time to event data, there are a variety of parametric models which could be used to extrapolate the survivor function. Each of these implies a different hazard function and in situations where there is moderate censoring, they can result in quite different extrapolations. Expert opinion on the long-term survival or other quantities could reduce model uncertainty. We present a general and easily implementable approach for including a variety of types of expert opinions. Expert opinion is incorporated by penalizing the likelihood function. Inference is performed in a Bayesian framework, however, this approach can also be implemented using frequentist methods. The issue of aggregating pooling expert opinions is also considered and included in the analysis. We validate the method against a previously published approach and include a worked example of this method. This work highlights that expert opinions can be implemented in a straightforward manner using this approach, however, more work is required on the correct elicitation of these quantities.