Highlights
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Expected value of sample information (EVSI) quantifies the expected value to a decision maker of reducing uncertainty through a given data collection exercise, such as a randomized clinical trial. In this article, we address the problem of computing EVSI when we assume treatment effect waning or use flexible survival models, by developing general-purpose methods that standardize and reduce the computational burden of the EVSI data-generation step for survival data.We developed 2 methods for simulating survival data from a probabilistic sample of survival probabilities over discrete time units, a discrete sampling method and an interpolated inverse transform sampling method, which can be combined with a recently proposed nonparametric EVSI method to accurately estimate EVSI for collecting survival data.Our general-purpose data-simulation methods greatly reduce the computational burden of the EVSI data-simulation step when we assume treatment effect waning or use flexible survival models. The implementation of our data-simulation methods is identical across all possible survival models and can therefore easily be automated from standard probabilistic decision analyses.Expected value of sample information (EVSI) quantifies the expected value to a decision maker of reducing uncertainty through a given data collection exercise, such as a randomized clinical trial.Sufficient evidence on time-to-event outcomes, such as overall survival (OS) and time to progression, is crucial for accurately determining the long-term effects of new treatments.In this article, we address the problem of computing EVSI when we assume treatment effect waning or use flexible survival models, by developing general-purpose methods that standardize and reduce the computational burden of the EVSI data-generation step for survival data. We develop a discrete sampling method and an interpolated ITS method for simulating survival data from a probabilistic sample of survival probabilities over discrete time units. The discrete sampling method samples time cycles using the survival probabilities and sets the event times to the half-cycle times. The interpolated ITS method extends this to continuous time by initially sampling random uniform numbers between 0 and 1 and then interpolating the survival probabilities using cubic splines at the sampled numbers and recording the interpolated cycle times. We demonstrate in an illustrative case study that, when the general-purpose data-simulation methods are combined with a recently proposed nonparametric EVSI method,The article is structured as follows. In the second section, we first describe the standard ITS method and then introduce the general-purpose methods for simulating survival data. In the third section, we compare the standard ITS method and the general-purpose data-simulation methods based on an illustrative partitioned survival model for scenarios with and without adjustment for treatment effect waning. In the final section, we conclude with a short discussion.