Slipping Away: Slippage in Hazard Ratios Over Datacuts and Its Impact on Immuno-oncology Combination Economic Evaluations

• DOI: 10.1016/j.jval.2024.09.008
• Publication Date: 2024-10-09T20:21:06Z
• Authors: Dawn Lee; Zain Ahmad; Caroline Farmer; Maxwell S. Barnish; Alan Lovell; G.J. Melendez-Torres

This study examines the impact of slippage in hazard ratios (tending towards the null over subsequent datacuts) for overall survival for combination treatment with a PD-(L)-1 inhibitor and a tyrosine kinase inhibitor (TKI) in advanced renal cell carcinoma (RCC).Four trials' Kaplan Meier curves were digitized over several datacuts and fitted with standard parametric curves. Accuracy and consistency of early data projections were calculated versus observed restricted mean survival time (RMST) and fitted lifetime survival from the longest follow-up datacut. The change in economically justifiable price (eJP) was calculated fitting the same curve to both arms, using an assumed average utility of 0.7 and willingness-to-pay threshold of £30,000 per QALY. The eJP represents the lifetime justifiable price increment for the new treatment, including differences in drug, administration and disease-related costs.Slippage in hazard ratios was observed in trials with longer follow-up, potentially influenced by subsequent PD-(L)-1 use after TKI monotherapy, early stoppage of PD-(L)-1 and development of resistance. Lognormal and log-logistic curves were more likely to over-predict the observed result; Gompertz and gamma under-predicted. Statistical measures for goodness of fit did not reliably predict the RMST. Large differences in incremental mean life years were observed between even the penultimate and final datacuts for the majority of the fitted curves, meaningfully impacting the eJP.This work demonstrates the challenge in predicting treatment benefits with novel therapies using immature data. Incorporating information on the impact of subsequent treatment is likely to play a key role in improving predictions.