Economic Modelling for Early Decision Making: Opportunity or Further Uncertainty

Problem Statement: Early decision-making is crucial for pharmaceutical/devices companies when developing oncology treatments given the substantial monetary investment required to conduct clinical trials and bring them to market. Understanding the key drivers of future value and data requirements is vital for better decisions.

Description: To facilitate early decision-making, a simplified, flexible, user friendly early economic model and pricing tool in R and rShiny was developed and tested in three case studies (optimistic, pessimistic, neutral). This tool was designed to analyse early phase clinical data from patients with advanced oncology diseases with the aim of predicting economically justifiable drug prices (EJPs). It used a lifetime partitioned survival analysis approach based on overall survival and progression-free survival. Given the limited survival data at early phases, the tool accommodates either Kaplan-Meier curves or one to two survival data points. The ‘flexsurv’ packages from R 4.3.0 were used for the survival analyses. The case studies were selected from recent advanced oncology technology assessments by the National Institute for Health and Care Excellence. EJPs estimated based on Phase I/II trial data were compared with predictions using corresponding Phase III data under different survival curve assumptions. Results showed that the incorporation of external data on comparators, prognostic factors, and long-term survival assumptions not only enhanced the prediction of EJPs but also resulted in improved decision-making and a better understanding of the implications associated with clinical trial designs, including disease severity, treatment lines, combination treatments, and patient demographics.

Lessons Learned: There are significant uncertainties around using early clinical trial data in predicting future value in advanced oncology. Early economic modelling of EJP could reduce uncertainties, provide companies with valuable insights for the evidence generation plans, help with go/no go decisions from a cost-effectiveness perspective.

Stakeholder perspective: The case study was carried out from an industry perspective.