Unravelling the Association Between Uncertainties in Model-based Economic Analysis and Funding Recommendations of Medicines in Australia.

Objective: Health technology assessment is used extensively by the Pharmaceutical Benefits Advisory Committee (PBAC) to inform medicine funding recommendations in Australia. The PBAC often does not recommend medicines due to uncertainties in economic modelling that result in delaying access to medicines for patients. The systematic identification of which uncertainties can be reduced with alternative evidence or the collection of additional data can help inform recommendations. This study aims to characterise different types of uncertainty in economic models and empirically assess their association with the PBAC recommendations.

Methods: A framework was developed to characterise four types of uncertainties: methodological, structural, generalisability and parameter uncertainty. The first two types were further subcategorised into parameterisable and unparameterisable uncertainty. Data on uncertainty and other factors were extracted from PBAC's Public Summary Documents of first submissions for 193 medicine (vaccine)-indication pairs including economic modelling between 2014 and 2021. Logistic regression was used to estimate the average marginal effect of each type of uncertainty on the probability of a positive recommendation.

Results: The PBAC more often raised issues regarding parameter uncertainty (95%) and parameterisable structural uncertainty (83%) than generalisability uncertainty (48%) and unparameterisable methodological uncertainty (56%). The logistic regression results suggested that the PBAC was more likely to recommend a medicine without unparameterisable methodological, generalisability, and parameterisable structural uncertainty by 15.0%, 10.2 %, and 17.6%, respectively. Parameterisable methodological, unparameterisable structural and parameter uncertainty were not significantly associated with the PBAC recommendations.

Conclusions: This study identified the uncertainties that had significant associations with PBAC recommendations based on the first submission. This may help improve model quality and reduce resubmissions in the future, thus improving patients' access to medicines.