1582O - Developing a real-world outcomes forecast model using matched oncology clinical trials and real world evidence to inform policy-making and reimbursement approaches

Background

Payers and pharmaceutical companies increasingly consider evidence-linked market access schemes or outcome-based pricing models, where access or payments depend on real-world outcomes and involve financial risk-sharing. A recognized barrier to implementation has been the uncertainty of basing policy or financial decisions on clinical trial results that may inadequately predict a product’s performance in real-world settings or populations. To inform such models and policy decisions, stakeholders would ideally be able to forecast financial impact prior to implementation. Doing so also protects potential down-side financial risk—a key consideration in health systems reliant on public funds with responsibility for prudent budget management. We therefore sought to develop a methodology for forecasting a range of reasonably expected real-world clinical outcomes (RWCOs) using published data from recent oncology clinical trials and post-market studies.

Methods

Using a database of 40 follow-on, longitudinal outcomes studies from 2008-2019 in a large US community oncology network, we identified and matched phase 3 trials and cohorts to real-world studies to compile a dataset. To approach forecasting a range of reasonably expected RWCOs for the identified clinical trials in this dataset, we used nonlinear regression analysis and Monte Carlo simulation to fit and stress-test a curve across the matched cohorts and outcomes.

Results

Through a series of comparisons between phase 3 trial results and corresponding, matched long-term follow-on study results, future RWCOs can potentially be simulated in order to set upper and lower bounds with utility for outcomes-based or risk-sharing pharmaceutical pricing market access models. Next steps include expanding and refining the scope of algorithms tested for their predictive utility and validating these models against other matched sets of clinical trial and post-market studies.

Conclusions

Forecasting and modeling tools that predict RWCOs would allow policy and pricing stakeholders to better understand financial impacts of RWE-based policy decisions and could mitigate stakeholders’ exposure to down-side financial risks.

Clinical trial identification

Editorial acknowledgement

Legal entity responsible for the study

The authors.

Funding

Has not received any funding.

Disclosure

T. Gorman: Full/Part-time employment: Lydion Research. A. Ray: Leadership role, Full/Part-time employment, Officer/Board of Directors: Lydion Research. S. Brar: Full/Part-time employment: Lydion Research. J. Hinkel: Officer/Board of Directors: Lydion Research; Full/Part-time employment: Caris Life Sciences; Officer/Board of Directors: Global Campaign for Cancer Survivorship.