An Efficient Method for Computing Expected Value of Sample Information for Survival Data from an Ongoing Trial
Permanent lenke
https://hdl.handle.net/10037/24170Dato
2021-12-30Type
Journal articleTidsskriftartikkel
Peer reviewed
Forfatter
Vervaart, Mathyn Adrianus Marinus; Strong, Mark; Claxton, Karl; Welton, Nicky; Wisløff, Torbjørn; Aas, ElineSammendrag
Methods - We 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.
Results - There 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.
Conclusions - We 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.