A Bayesian joint model of multiple nonlinear longitudinal and competing risks outcomes for dynamic prediction in multiple myeloma: joint estimation and corrected two-stage approaches

TL;DR

This paper introduces a Bayesian joint modeling approach for multiple biomarkers and competing risks in multiple myeloma, enabling dynamic prediction of clinical events and comparing joint and two-stage estimation methods.

Contribution

It develops a novel Bayesian joint model for multiple longitudinal biomarkers and competing risks, with two estimation strategies, applied to real-world MM data for improved dynamic prediction.

Findings

The joint model accurately predicts time-to-events using longitudinal biomarker data.

The corrected two-stage estimation reduces computational time with comparable accuracy.

Model validation shows effective dynamic prediction in a real-world MM cohort.

Abstract

Predicting cancer-associated clinical events is challenging in oncology. In Multiple Myeloma (MM), a cancer of plasma cells, disease progression is determined by changes in biomarkers, such as serum concentration of the paraprotein secreted by plasma cells (M-protein). Therefore, the time-dependent behaviour of M-protein and the transition across lines of therapy (LoT) that may be a consequence of disease progression should be accounted for in statistical models to predict relevant clinical outcomes. Furthermore, it is important to understand the contribution of the patterns of longitudinal biomarkers, upon each LoT initiation, to time-to-death or time-to-next-LoT. Motivated by these challenges, we propose a Bayesian joint model for trajectories of multiple longitudinal biomarkers, such as M-protein, and the competing risks of death and transition to next LoT. Additionally, we explore two estimation approaches for our joint model: simultaneous estimation of all parameters (joint estimation) and sequential estimation of parameters using a corrected two-stage strategy aiming to reduce computational time. Our proposed model and estimation methods are applied to a retrospective cohort study from a real-world database of patients diagnosed with MM in the US from January 2015 to February 2022. We split the data into training and test sets in order to validate the joint model using both estimation approaches and make dynamic predictions of times until clinical events of interest, informed by longitudinally measured biomarkers and baseline variables available up to the time of prediction.