Aims
This study predicts the potential impact of novel therapies on the patient eligibility, resource allocation, and treatment pathways for colorectal (CRC), melanoma (MEL), and non-small cell lung (NSCLC) cancers. These insights support health technology assessment bodies and policymakers in optimising resource planning, reimbursement decisions, and patient access to novel, effective medicines. We illustrate our results with CRC.
Methods
We used our comprehensive Victorian linked-dataset, which includes Victorian Cancer Registry records linked to PBS, MBS, and hospital data to develop discrete-event simulation (DES) models for each cancer type. These models capture the current standard of care in Australia, estimating the number of patients treated per stage and line of treatment. Forecast incidence, stage at diagnosis distribution, and mutation prevalence are integrated for robust estimates. Horizon scan (HS) results shape scenario analyses to evaluate future therapy impacts.Â
Results
Our forecasts suggest that from 2022 to 2026, 116,753 colorectal cancer treatments will be provided across all stages in Australia, with 43% representing advanced disease. Scenario analysis of the introduction of pembrolizumab for deficient mismatch-repair in metastatic CRC as first-line treatment would result in 706 patients per year being treated, considering a full uptake and a hazard ratio of 0.6 for the time-to-progression.
Conclusion
Our forecasting analysis suggests an increase in the proportion of patients’ progression to further lines of treatment with the introduction of pembrolizumab. However, an extended time-to-progression counterbalances this increase, thereby maintaining similar levels of downstream treatment utilisation in second-to-fourth lines. Hence, the addition of pembrolizumab to the first-line treatment regimen appears to offer clinical benefits to patients without significantly escalating treatment demand over a 5-year time horizon. Other HS outputs, such as relatlimab in melanoma and lorlatinib in NSCLC, were used to predict future impact.