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

2025-12-18

AI Summary

Immature overall survival (OS) data in early-phase oncology trials lead to biased extrapolation using conventional methods. Method: We propose a Bayesian joint modeling framework integrating longitudinal sum-of-longest-diameters (SLD) measurements from NTRK-fusion–positive solid tumor patients, coupling a linear mixed-effects model for tumor dynamics with a Cox–Weibull survival model. Innovatively, we introduce a cross-tumor-type hierarchical exchangeable association structure—first applied in the larotrectinib trial—incorporating stratified random effects and posterior inference via Markov Chain Monte Carlo (MCMC). Results: SLD exhibits a statistically significant association with mortality: each 10-mm increase elevates hazard by 9% (HR = 1.09, 95% CI: 1.02–1.17). Compared to standard Weibull models, our approach yields more precise parameter estimates and demonstrates high concordance with latest data cut-offs. The framework robustly supports multi-biomarker integration and enables individualized, early-stage OS prediction.

Technology Category

• Reasoning under Uncertainty: Relational Probabilistic Models
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• User Modeling, Personalization and Recommendation: Studies of user behavior, including longitudinal effects of personalized systems
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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.

Problem

• Predict overall survival using Bayesian joint modeling with biomarkers
• Compare Bayesian approach with traditional parametric survival extrapolation
• Apply method to larotrectinib trial data for NTRK+ solid tumors

Innovation

• Bayesian joint modeling of longitudinal biomarkers for survival prediction
• Using tumor burden data to extrapolate overall survival outcomes
• Hierarchical modeling accommodating multiple tumor types and biomarkers