Joint models for multiple longitudinal processes and time-to-event outcome

ABSTRACT

Joint models are statistical tools for estimating the association between time-to-event and longitudinal outcomes. One challenge to the application of joint models is its computational complexity. Common estimation methods for joint models include a two-stage method, Bayesian and maximum-likelihood methods. In this work, we consider joint models of a time-to-event outcome and multiple longitudinal processes and develop a maximum-likelihood estimation method using the expectation–maximization algorithm. We assess the performance of the proposed method via simulations and apply the methodology to a data set to determine the association between longitudinal systolic and diastolic blood pressure measures and time to coronary artery disease.

KEYWORDS:

• Joint models
• EM algorithm
• simulation
• multiple longitudinal outcomes
• time-to-event outcome

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

The research is supported by National Institutes of Health (NIH) [grant numbers R01 AG019181, R24 MH080827, and P30 AG10133].