An Efficient Method for Computing Expected Value of Sample Information for Survival Data from an Ongoing Trial

Abstract

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.