Strengths and Limitations
CHRONIC DISEASES
We developed an interpolated ITS method and a discrete sampling method for simulating survival data from a probabilistic sample of survival probabilities over discrete time units. Our general-purpose methods greatly reduce the computational burden of the standard ITS method when closed-form expressions for the quantile function are unavailable, such as for custom distributions that incorporate assumptions about treatment effect waning as commonly encountered in practice,Generally, the precision of the EVSI estimator is influenced by the number of simulated data sets and the effective sample size of the simulated data. The discrete sampling method and, to a lesser degree, the interpolated ITS method, additionally introduce an approximation error that depends on the cycle length. It is generally recommended that discrete-time health economic models use a short cycle length to reduce the discrete-time approximation error, which could be as short as 1 week for slowly progressing chronic diseases.We structured the synthetic case study around a PSM, a type of model that is frequently used to inform reimbursement decisions for new oncology drugs.If individual patient data are available with a similar study design and at least the same length of follow-up as the proposed study, study data sets could alternatively be simulated using a 2-level resampling method based on bootstrapping.The key notion behind EVSI is that the prior distribution of the model parameters is updated with simulated study data to estimate the joint posterior distribution given both prior information and the simulated study data. In EVSI analyses, the prior distribution is often informed by external evidence, such as digitized Kaplan–Meier data. This may, however, not match the way in which real-world analyses of study data are conducted, since these may not synthesize the collected study data and the external evidence. Analysts should therefore ensure that the way in which the study data is analyzed once it has been collected is aligned with the assumptions underpinning the EVSI analysis.Despite its routine application in cost-effectiveness analyses, there is currently a lack of guidance on how to model treatment effect waning.