Evaluating the accuracy of extrapolated overall survival for pembrolizumab: a comparison with long-term observed data in the Australian reimbursement context
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View abstract on PubMed
Summary
This summary is machine-generated.Statistical models predicting cancer drug survival often underestimate real outcomes. This study found both Sponsor and PBAC models for pembrolizumab tended to underestimate long-term patient survival, with PBAC models being more conservative.
Area Of Science
- Health Economics
- Oncology
- Biostatistics
Background
- Government funding decisions for new medicines rely on statistical models to predict patient survival.
- Overall survival (OS) predictions are crucial for assessing the cost-effectiveness of cancer drugs.
- Accuracy of these predictive models is vital for informed healthcare policy.
Purpose Of The Study
- To compare the accuracy of statistical models used by pharmaceutical Sponsors and the Pharmaceutical Benefits Advisory Committee (PBAC).
- To evaluate these models against real-world long-term follow-up (LTFU) data for the cancer drug pembrolizumab.
- To assess survival prediction accuracy for pembrolizumab across various indications.
Main Methods
- Reviewed publicly available PBAC Summary Documents (PSDs) for pembrolizumab funding decisions up to November 2022.
- Included cases with at least three years of follow-up and 350+ patients treated annually.
- Compared survival predictions from Sponsor and PBAC models against actual survival data at two time points.
Main Results
- Five out of 15 pembrolizumab indications met the inclusion criteria.
- Sponsor models underestimated real survival by 0.54% to 16.45%.
- PBAC models underestimated real survival by 1.20% to 24.21%.
Conclusions
- Both Sponsor and PBAC OS extrapolation methods tend to underestimate long-term survival for pembrolizumab.
- PBAC-preferred models demonstrated a more conservative underestimation of survival.
- Findings highlight the need for refining predictive models in drug funding decisions.
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