It has already been pointed out that the bootstrap can be used to calculate the expected value of perfect information (EVPI) when individual-level data from a randomized controlled trial (RCT) is at hand. However, as mentioned by others, it is not clear if and how such a method can be extended to calculate the expected value of sample information (EVSI). In this article, we provide a nonparametric definition for EVPI and EVSI, which is based on considering the entire population distribution as the uncertain entity for which the current RCT provides partial information. This enables a theoretical justification for using the bootstrap for EVPI calculation and allows us to propose a two-level resampling method for EVSI calculation. What is considered as the sampling unit in this algorithm can range from the individual level net benefits to the full panel of the RCT data for an individual, enabling the analyst to decide on a trade-off between computational efficiency and comprehensiveness in value of information analysis. As such, we argue that this method, in addition to being consistent with the popular bootstrap method of RCT-based economic evaluations, is a flexible approach for EVPI and EVSI calculations.