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

Lili Yang, Menggang Yu, Sujuan Gao

Research output: Contribution to journalArticle

1 Citation (Scopus)

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.

Original languageEnglish (US)
Pages (from-to)1-19
Number of pages19
JournalJournal of Statistical Computation and Simulation
DOIs
StateAccepted/In press - May 7 2016

Fingerprint

Joint Model
Maximum likelihood estimation
Blood pressure
Coronary Artery Disease
Maximum likelihood
Computational complexity
Maximum Likelihood Method
Blood Pressure
Expectation-maximization Algorithm
Bayesian Methods
Maximum Likelihood Estimation
Simulation Methods
Computational Complexity
Methodology

Keywords

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

ASJC Scopus subject areas

  • Applied Mathematics
  • Statistics and Probability
  • Modeling and Simulation
  • Statistics, Probability and Uncertainty

Cite this

Joint models for multiple longitudinal processes and time-to-event outcome. / Yang, Lili; Yu, Menggang; Gao, Sujuan.

In: Journal of Statistical Computation and Simulation, 07.05.2016, p. 1-19.

Research output: Contribution to journalArticle

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