Bayesian joint modelling of longitudinal biomarkers to enable extrapolation of overall survival: an application using larotrectinib trial clinical data

TL;DR

This paper demonstrates that Bayesian joint modelling of longitudinal biomarkers can improve overall survival predictions in clinical trials, especially with limited follow-up data, by integrating intermediate outcomes and hierarchical data structures.

Contribution

The study introduces a Bayesian joint modelling approach for survival prediction using intermediate biomarkers, accounting for multiple tumour sites and hierarchical data structures, outperforming traditional methods.

Findings

Bayesian joint model estimates are more certain than Weibull models.

Tumour-specific survival estimates are consistent across models for larger tumours.

Intermediate biomarker-based predictions align well with later observed survival data.

Abstract

Objectives To investigate the use of a Bayesian joint modelling approach to predict overall survival (OS) from immature clinical trial data using an intermediate biomarker. To compare the results with a typical parametric approach of extrapolation and observed survival from a later datacut. Methods Data were pooled from three phase I/II open-label trials evaluating larotrectinib in 196 patients with neurotrophic tyrosine receptor kinase fusion-positive (NTRK+) solid tumours followed up until July 2021. Bayesian joint modelling was used to obtain patient-specific predictions of OS using individual-level sum of diameter of target lesions (SLD) profiles up to the time at which the patient died or was censored. Overall and tumour site-specific estimates were produced, assuming a common, exchangeable, or independent association structure across tumour sites. Results The overall risk of mortality was 9% higher per 10mm increase in SLD (HR 1.09, 95% CrI 1.05 to 1.14) for all tumour sites combined. Tumour-specific point estimates of restricted mean , median and landmark survival were more similar across models for larger tumour groups, compared to smaller tumour groups. In general, parameters were estimated with more certainty compared to a standard Weibull model and were aligned with the more recent datacut. Conclusions Joint modelling using intermediate outcomes such as tumour burden can offer an alternative approach to traditional survival modelling and may improve survival predictions from limited follow-up data. This approach allows complex hierarchical data structures, such as patients nested within tumour types, and can also incorporate multiple longitudinal biomarkers in a multivariate modelling framework.