  The European Medicines Agency has in recent years allowed licensing of new
pharmaceuticals at an earlier stage in the clinical trial process. When trial
evidence is obtained at an early stage, the events of interest, such as disease
progression or death, may have only been observed in a small proportion of
patients. Health care authorities therefore must decide on the adoption of new
technologies based on less mature evidence than previously, resulting in
greater uncertainty about clinical- and cost-effectiveness. When a trial is
ongoing at the point of decision making, there may be value in continuing the
trial in order to collect additional data before making an adoption decision.
This can be quantified by the Expected Value of Sample Information (EVSI).
However, no guidance exists on how to compute the EVSI for survival data from
an ongoing trial, nor on how to account for uncertainty about the choice of
survival model in the EVSI calculations. In this article, we describe
algorithms for computing the EVSI of extending a trial's follow-up, both where
a single known survival model is assumed, and where we are uncertain about the
true survival model. We compare a nested Markov Chain Monte Carlo procedure
with a non-parametric regression-based method in two synthetic case studies,
and find close agreement between the two methods. The regression-based method
is fast and straightforward to implement, and scales easily to include any
number of candidate survival models in the model uncertainty case. EVSI for
ongoing trials can help decision makers determine whether early patient access
to a new technology can be justified on the basis of the current evidence or
whether more mature evidence is needed.
