  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.
