Joint Modeling of Longitudinal Systolic and Diastolic Blood Pressure Measurements of Hypertensive Patients Receiving Treatment


Hypertension is a chronic disease that has a major health problem over the
centuries due to its signicant contribution to the global health burden. It
is also called high blood pressure, described by two numbers Systolic blood
pressure (SBP) and diastolic blood pressure (DBP). Hence, joint longitudinal
model was used to address how the evolution of SBP is associated with the
evolution of DBP. The objective was to investigate the joint evolution and
association of SBP and DBP measurements of hypertensive patients and
identify the potential risk factors aecting the two end points. In this this
study 354 hypertensive patients with age greater than or equal to 18 years,
who were on treatment, and who had measured at least three times were
included. For a close examination of the separate and joint models, rst, each
of the outcomes was analyzed separately using linear mixed model. Then,
a joint model was considered to study the joint evolution and identify the
potential risk factors aecting the two end points. Fit statistics showed that
the joint model resulted in better t to the data than the separate models.
Based on the joint model, sex, baseline age, and place of residence were the
signicant factors for the progression of blood pressure, while family history
and all the interaction term except age by time did not appear signicant.
The result from the joint model suggested a strong association between the
evolutions and a slowly increasing evolution of the association between SBP
and DBP.

DOI Code: 10.1285/i20705948v7n2p308

Keywords: Joint Modeling, Longitudinal Data Analysis, Linear Mixed Model


Giles, T., Berk, B. and Black, H. (2005). On behalf of the Hypertension Writing Group. Expanding the definition and classification of hypertension, Clin Hypertens; 89:505-512.

Laird, N., and Ware, J. (1982). Random-effects models for longitudinal data. Biometrics

Molenberghs, G. and Verbeke, G. (2005). Models for Discrete Longitudinal Data. Springer Science+Business Media, Inc

Gebregziabher M, Egede LE, Lynch CP, Echols C and Zhao Y (2010). Effect of trajectories of glycemic control on mortality in type 2 diabetes: a semiparametric joint modeling approach. Am J Epidemiol. 171:1090-8.

Tsiatis, A. and Davidian, M. (2004) An overview of joint modeling of longitudinal and time-to- event data. Statistica Sinica 14, 793-818.

Cox, D. R., and Wermuth, N. (1992) Response models for mixed binary and quantitative variables. Biometrika, 79, 441-461.

Full Text: pdf

Creative Commons License
This work is licensed under a Creative Commons Attribuzione - Non commerciale - Non opere derivate 3.0 Italia License.