A Bayesian Model Leveraging Multiple External Data Sources to Improve the Reliability of Lifetime Survival Extrapolations in Metastatic Non-Small-Cell Lung Cancer

Abstract

Objectives

Bayesian multiparameter evidence synthesis (B-MPES) can improve the reliability of long-term survival extrapolations by leveraging registry data. We extended the B-MPES framework to also incorporate historical trial data and examined the impact of alternative external information sources on predictions from early data cuts for a trial in metastatic non-small-cell lung cancer (mNSCLC).

Methods

B-MPES models were fitted to survival data from the phase III CheckMate 9LA study of nivolumab plus ipilimumab plus 2 cycles of chemotherapy (NIVO+IPI+CHEMO, v. 4 cycles of CHEMO) in first-line mNSCLC, with 1 y of minimum follow-up. Trial observations were supplemented by registry data from the Surveillance, Epidemiology, and End Results program, general population data, and, optionally, historical trial data with extended follow-up for first-line NIVO+IPI (v. CHEMO) and/or second-line NIVO monotherapy in advanced NSCLC, via estimated 1-y conditional survival. Predictions from the 3 alternative B-MPES models were compared with those from standard parametric models (SPMs).

Results

B-MPES models better anticipated the emergent survival plateau with NIVO+IPI+CHEMO that was apparent in the 4-y data cut compared with SPMs, for which short-term extrapolations in both treatment arms were overly conservative. However, the B-MPES model incorporating NIVO+IPI data slightly overestimated 4-y NIVO+IPI+CHEMO survival owing to a confounding effect on estimated hazards that could not be accounted for a priori until later data cuts of CheckMate 9LA. Extrapolations were relatively robust to the choice of external data sources provided that the prior data had been adjusted to attenuate confounding.

Conclusions

Incorporating historical trial data into survival models can improve the plausibility and interpretability of lifetime extrapolations for studies of novel therapies in metastatic cancers when data are immature, and B-MPES provides an appealing method for this purpose.

Highlights

• Leveraging historical trial data with extended follow-up to extrapolate survival from early study data cuts in a Bayesian evidence synthesis framework can realize anticipated longer-term effects that are characteristic of a novel therapy or class thereof.

• Using moderately confounded external data sources can improve the reliability of survival extrapolations from B-MPES models provided that the prior information is adjusted and rescaled appropriately, but it is essential to rationalize the implicit assumptions surrounding longer-term treatment effects in the current study.

• B-MPES models are an attractive option to conduct informed lifetime survival extrapolations based on transparent clinical assumptions via leveraging multiple external data sources, but model flexibility and a priori confidence in external data must be specified carefully to avoid overfitting.