Abstract
Joint models for longitudinal and time to event data are frequently used in many observational studies such as clinical trials with the aim of investigating how biomarkers which are recorded repeatedly in time are associated with time to an event of interest. In most cases, these joint models only consider a univariate time to event process. However, many clinical trials of patients with cancer, involve multiple recurrences of a single event together with a single terminal event experienced by patients over time. Therefore, this article proposes joint modelling approachs for longitudinal and multi-state data. The approach considers two sub-models that are linked by a common latent random variable. The first sub-model is linear mixed effect model that defines the longitudinal process and the second sub-model is a proportional intensity function for the multi-state process. Furthermore, on the proportional intensity model, two different formulations are used to define dependence structure between longitudinal and multi-state processes. In this article, a semi-Markov process that consider the time spent in the current state is defined for the transitions between states. Moreover, the time spent in each transient state is assumed to have Gompertz distribution. A Bayesian method using Markov Chain Monte Carlo (MCMC) is developed for parameter estimation and inferences. The deviance information criterion (DIC) is also derived for Bayesian model selection and comparison. Finally, our proposed joint modeling approach is evaluated through a simulation study and is applied to real datasets (colorectal and colorectal.Longi) which present a random selection of 150 patients from a multi-center randomized phase III clinical trial FFCD 2000-05 of patients diagnosed with metastatic colorectal cancer.
Highlights
In many biomedical studies such as HIV/AIDS studies, subjects’ biomarkers are collected repeatedly over time together with the time to occurrence of a clinical event
Our proposed joint modeling approach is evaluated through a simulation study and is applied to real datasets which present a random selection of 150 patients from a multi-center randomized phase III clinical trial Federation Francophone de Cancerologie Digestive (FFCD) 2000-05 of patients diagnosed with metastatic colorectal cancer
Joint models of longitudinal and multi-state data are less explored in literature
Summary
In many biomedical studies such as HIV/AIDS studies, subjects’ biomarkers are collected repeatedly over time together with the time to occurrence of a clinical event. In prostate cancer , a subject can be followed after cancer treatment and at each clinical visit, biomarker (prostate specific antigen) called longitudinal measurements are recorded together with the time to re-occurrence of cancer (Yu et al, 2008) Another example include the study of time to re-hospitalization by Dendale et el. The most recently study on such joint models was proposed by Ferrer et al (2016) Their approach introduces a shared random effects variable for longitudinal measurements and times of transitions between states. To the best of our knowledge, there exist no joint model for longitudinal and multi-state data (recurrent events with death as terminal event) under the framework of Bayesian inference.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: International Journal of Statistics and Probability
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
