MS4 A COMPARISON OF THE PARTITIONED SURVIVAL AND MULTISTATE EXTRAPOLATION METHODS: A CASE STUDY BASED ON THE GASTRIC CANCER TRIAL ATTRACTION-2

Abstract

Partitioned survival modelling (PartSA) is frequently used in cost-effectiveness analysis in oncology and is characterized by separate modelling of time to event (TTE) outcomes. The multistate modelling (MSM) approach is defined by its ability to jointly estimate TTEs. The aim of this study was to compare PartSA and MSM-obtained survival extrapolations using data from the ATTRACTION-2 (NCT02267343) trial, which compared nivolumab with best supportive care (BSC) in advanced or metastatic gastric cancer. Two data cutoffs were available for ATTRACTION-2, from August 2016 and February 2018, with median follow-up periods of 9 and 24 months, respectively. Standard parametric models were fitted to the 2016 data using the PartSA and MSM approaches. The MSM approach included a covariate to account for patients’ history of time on treatment. Extrapolations for overall survival (OS) based on 2016 data were compared with the more mature 2018 data. The observed restricted mean survival (RMS) at 3 years in ATTRACTION-2 was 0.50 years (95% CI: 0.42-0.58 years) and 0.78 years (95% CI: 0.70-0.87 years) for BSC and nivolumab, respectively (2018 data). Both modelling approaches using the 2016 data successfully predicted the observed OS in the 2018 data, as the 3-year RMS estimates were within the observed 95% CIs. OS estimates for BSC were similar for both approaches (3-year RMS, PartSA: 0.51 years; MSM: 0.52 years). OS estimates for nivolumab were lower using PartSA than MSM (PartSA: 0.77 years; MSM: 0.85 years). Both PartSA and MSM, based on the 2016 data, provided accurate predictions up to three years as estimates were within the observed 95% CI of the 2018 data. MSM predicted a higher mean survival for nivolumab compared to PartSA, as MSM predicted a long-term survival plateau whereas PartSA did not. Clinical insight could provide additional guidance on the preferred extrapolation approach.