Rotterdam, The Netherlands – The current practice of prioritizing research in the context of conditional reimbursement can lead to wrong decisions since it relies on randomized controlled trials (RCT) based cost-effectiveness models, which do not reflect all uncertainties that are crucial in daily clinical practice.
Treatment persistence, compliance, and broadening of indication can add substantial extra uncertainty to decisions about reimbursing treatments, according to a new study by the Institute for Medical Technology Assessment (iMTA) of Erasmus University Rotterdam. These uncertainties should be included in valuation-of-information analyses that are conducted to prioritize additional research, but usually they are not, as they are hardly relevant in RCTs. The additional ‘daily practice parameters’ do not merely add uncertainty, individually; they also interact with the RCT-based uncertainties, which may increase their impact.
“Based on our experience with conditional reimbursement dossiers we know that decision-analytic models included in those dossiers do not properly represent daily practice. Thus, if the need for additional research is assessed at the beginning of the period of conditional reimbursement and this assessment is based solely on the RCT-based model, decisions can be very wrong.” said author Isaac Corro Ramos, PhD.
Under the Dutch conditional reimbursement scheme, expensive new treatments can be accepted for reimbursement, provided that additional data is collected over a 4-year period in order to reduce uncertainty about the cost-effectiveness. Value-of-information analysis can be performed to decide which data is most relevant for increasing confidence in a correct reimbursement decision.
The full study, “Determining the Impact of Modeling Additional Sources of Uncertainty in Value-of-Information Analysis,” is published in Value in Health.