B0622
Title: A two-stage approach for joint modelling of competing risks and multiple longitudinal outcomes
Authors: Danilo Alvares - University of Cambridge (United Kingdom) [presenting]
Spyros Roumpanis - F Hoffmann-La Roche AG (Switzerland)
Francois Mercier - F Hoffmann-La Roche AG (Switzerland)
Sean Yiu - Roche Products Limited (United Kingdom)
Vallari Shah - F Hoffmann-La Roche Ltd (Switzerland)
Felipe Castro - F Hoffmann-La Roche Ltd (Switzerland)
Jessica Barrett - University of Cambridge (United Kingdom)
Yajing Zhu - F Hoffmann-La Roche AG (Switzerland)
Abstract: Recent trends in personalised healthcare have motivated great interest in the dynamic prediction of survival and other clinically important events by using baseline characteristics and the evolving history of disease progression. The methodological developments were motivated by a case study in multiple myeloma (a type of bone marrow cancer), where progression is assessed by several biomarker trajectories, and patients may experience multiple regimen changes over time. To understand the dynamic interplay between biomarkers and their connections to the survival process, a two-stage Bayesian joint model is developed for competing risks and multiple longitudinal outcomes. The proposal is applied to an observational study from the US nationwide Flatiron health electronic health record (EHR)-derived de-identified database, where patients diagnosed with multiple myeloma from January 2015 to February 2022 were selected. The data is split into training and test sets in order to assess the performance of the proposal in making dynamic predictions of times to events of interest (time to next line of therapy or time to death) using baseline variables and longitudinally measured biomarkers available up to the time of prediction. Individual weighted and Cox-Snell residuals validated the robustness of the model, and the Brier score supported its good predictive accuracy.
