Joint modelling of longitudinal and multi-state processes: application to clinical progressions in prostate cancer

TL;DR

This paper introduces a joint multi-state model linking longitudinal PSA measurements with prostate cancer progression, enabling detailed risk assessment of various relapse types and survival outcomes.

Contribution

It presents a novel joint model for multi-state processes and longitudinal data, with estimation via EM algorithm, extending existing R packages for clinical cancer progression analysis.

Findings

Model accurately predicts transition risks based on PSA trajectories.

Validated through simulations and applied to prostate cancer cohorts.

Quantifies impact of PSA and covariates on health state transitions.

Abstract

Joint modelling of longitudinal and survival data is increasingly used in clinical trials on cancer. In prostate cancer for example, these models permit to account for the link between longitudinal measures of prostate-specific antigen (PSA) and the time of clinical recurrence when studying the risk of relapse. In practice, multiple types of relapse may occur successively. Distinguishing these transitions between health states would allow to evaluate, for example, how PSA trajectory and classical covariates impact the risk of dying after a distant recurrence post-radiotherapy, or to predict the risk of one specific type of clinical recurrence post-radiotherapy, from the PSA history. In this context, we present a joint model for a longitudinal process and a multi-state process which is divided into two sub-models: a linear mixed sub-model for longitudinal data, and a multi-state sub-model with proportional hazards for transition times, both linked by shared random effects. Parameters of this joint multi-state model are estimated within the maximum likelihood framework using an EM algorithm coupled to a quasi-Newton algorithm in case of slow convergence. It is implemented under R, by combining and extending the mstate and JM packages. The estimation program is validated by simulations and applied on pooled data from two cohorts of men with localized prostate cancer and treated by radiotherapy. Thanks to the classical covariates available at baseline and the PSA measurements collected repeatedly during the follow-up, we are able to assess the biomarker's trajectory, define the risks of transitions between health states, and quantify the impact of the PSA dynamics on each transition intensity.