Joint longitudinal and time-to-event models for multilevel hierarchical data

TL;DR

This paper introduces a novel Bayesian joint model for hierarchical longitudinal and time-to-event data, specifically applied to tumor burden and survival in lung cancer, accommodating complex multi-level data structures.

Contribution

It proposes a new joint modeling approach for three-level hierarchical data, including novel association structures and software implementation.

Findings

Model effectively captures hierarchical data structure.

Provides user-friendly Bayesian software.

Applicable to complex multi-level clinical data.

Abstract

Joint modelling of longitudinal and time-to-event data has received much attention recently. Increasingly, extensions to standard joint modelling approaches are being proposed to handle complex data structures commonly encountered in applied research. In this paper we propose a joint model for hierarchical longitudinal and time-to-event data. Our motivating application explores the association between tumor burden and progression-free survival in non-small cell lung cancer patients. We define tumor burden as a function of the sizes of target lesions clustered within a patient. Since a patient may have more than one lesion, and each lesion is tracked over time, the data have a three-level hierarchical structure: repeated measurements taken at time points (level 1) clustered within lesions (level 2) within patients (level 3). We jointly model the lesion-specific longitudinal trajectories and patient-specific risk of death or disease progression by specifying novel association structures that combine information across lower level clusters (e.g. lesions) into patient-level summaries (e.g. tumor burden). We provide user-friendly software for fitting the model under a Bayesian framework. Lastly, we discuss alternative situations in which additional clustering factor(s) occur at a level higher in the hierarchy than the patient-level, since this has implications for the model formulation.