The analysis of multivariate longitudinal data using multivariate marginal models

• Published in: Journal of multivariate analysis Vol. 143; pp. 481 - 491
• Main Author: Cho, Hyunkeun
• Format: Journal Article
• Language: English
• Published: New York Elsevier Inc 01.01.2016; Taylor & Francis LLC
• Subjects: Generalized estimating equation; Longitudinal data; Longitudinal studies; Monte Carlo simulation; Multiple responses; Multivariate analysis; Multivariate marginal models; Normal distribution; Parameter estimation; Quadratic inference function; Studies; Variables; 62H15; Generalized estimating equation; Quadratic inference function; Longitudinal data; Multiple responses; 62H12; Multivariate marginal models
• ISSN: 0047-259X; 1095-7243
• DOI: 10.1016/j.jmva.2015.10.012

Longitudinal studies often involve multiple outcomes measured repeatedly from the same subject. The analysis of multivariate longitudinal data can be challenging due to its complex correlated nature. In this paper, we develop multivariate marginal models in longitudinal studies with multiple response variables, and improve parameter estimation by incorporating informative correlation structures. In theory, we show that the proposed method yields a consistent and efficient estimator which follows an asymptotic normal distribution. Monte Carlo studies indicate that the proposed method performs well in the sense of reducing bias and improving estimation efficiency. In addition, the proposed approach is applied to a real longitudinal data example of transportation safety with different response families.