Abstract
BackgroundDecisions about new health technologies are increasingly being made while trials are still in an early stage, which may result in substantial uncertainty around key decision drivers such as estimates of life expectancy and time to disease progression. Additional data collection can reduce uncertainty, and its value can be quantified by computing the expected value of sample information (EVSI), which has typically been described in the context of designing a future trial. In this article, we develop new methods for computing the EVSI of extending an existing trial’s follow-up, first for an assumed survival model and then extending to capture uncertainty about the true survival model.MethodsWe developed a nested Markov Chain Monte Carlo procedure and a nonparametric regression-based method. We compared the methods by computing single-model and model-averaged EVSI for collecting additional follow-up data in 2 synthetic case studies.ResultsThere was good agreement between the 2 methods. The regression-based method was fast and straightforward to implement, and scales easily to include any number of candidate survival models in the model uncertainty case. The nested Monte Carlo procedure, on the other hand, was extremely computationally demanding when we included model uncertainty.ConclusionsWe present a straightforward regression-based method for computing the EVSI of extending an existing trial’s follow-up, both where a single known survival model is assumed and where we are uncertain about the true survival model. 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.HighlightsDecisions about new health technologies are increasingly being made while trials are still in an early stage, which may result in substantial uncertainty around key decision drivers such as estimates of life-expectancy and time to disease progression. Additional data collection can reduce uncertainty, and its value can be quantified by computing the expected value of sample information (EVSI), which has typically been described in the context of designing a future trial.In this article, we have developed new methods 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 extend a previously described nonparametric regression-based method for computing EVSI, which we demonstrate in synthetic case studies is fast, straightforward to implement, and scales easily to include any number of candidate survival models in the EVSI calculations.The EVSI methods that we present in this article can quantify the need for collecting additional follow-up data before making an adoption decision given any decision-making context.
Highlights
The Expected Value of Sample Information (EVSI) quantifies the expected value to the decision maker of reducing uncertainty through the collection of additional data,[1,2] for example a future randomised controlled trial
In the last decade the European Medicines Agency (EMA) has introduced regulatory mechanisms that are aimed at accelerating licensing of new pharmaceuticals, such as ‘adaptive pathways’[4] and ‘conditional marketing authorisations.’[5]. When evidence is obtained from a trial 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
When a trial is ongoing at the point of decision making, for example when follow-up is continued for regulatory purposes, there may be value in delaying the adoption decision until additional data has been collected in the ongoing trial and uncertainty has reduced.[7]
Summary
The Expected Value of Sample Information (EVSI) quantifies the expected value to the decision maker of reducing uncertainty through the collection of additional data,[1,2] for example a future randomised controlled trial. Health care authorities have to issue guidance on new pharmaceuticals based on less mature evidence than previously, resulting in greater uncertainty about clinical- and cost-effectiveness With this comes an increased risk of recommending a technology that reduces net health benefit.[6]. When a trial is ongoing at the point of decision making, for example when follow-up is continued for regulatory purposes, there may be value in delaying the adoption decision until additional data has been collected in the ongoing trial and uncertainty has reduced.[7] In this context, there will be a trade-off between granting early access to a new technology that may turn out to reduce health benefits, and waiting for uncertainty to be reduced through ongoing data collection with a potential loss of health benefits while waiting. The value of delaying the decision could be quantified, at least in theory, by computing the EVSI for the additional follow-up data
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