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Joint Modeling of Longitudinal CD4 Count and Weight Measurements of HIV/Tuberculosis Co-infected Patients at Jimma University Specialized Hospital

Joint Modeling of Longitudinal CD4 Count and Weight Measurements of HIV/Tuberculosis Co-infected... As HIV/TB co-infected patients are started to be visited, it is common to measure weight and CD4 repeatedly overtime to determine the health status of patients. Most of the time linear mixed modeling of weight and CD4 count cannot handle the association between the outcomes whereas the joint modeling of multivariate linear mixed model does. Thus, this study was an attempt to model jointly the longitudinal CD4 and weight measurements of HIV/TB co-infected patients. This retrospective study consists of 254 HIV/TB co-infected patients who were 18 years old and above, and on ART followup from 1st February 2009 to 1st July 2014 at Jimma University Specialized Hospital. Firstly, weight and square root of CD4 count were analyzed separately. Based on the separate model, the joint models were built to know the correlation between mean change of weight and CD4 count overtime. Finally, appropriate model was selected based on AIC and BIC values. The fit statistics showed that the joint model fitted the data better than the separate model. From the joint model sex, educational level and functional status were the factors contributing to the prediction of HIV/TB co-infected patients weight at baseline. Beside the linear time effect has a positive effect on the mean change of weight whereas the quadratic time change has negative effect. The baseline CD4 count was differ by patient status and functional status. Further, the linear time effect has a positive sign and found to be statistically significant at 5 % level of significance on the mean change of the square root of CD4 count. Nevertheless, the quadratic time effect has a significant negative effect. The finding of the current study revealed that there was a moderate positive association between the mean change of weight and square root of CD4 count overtime. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Annals of Data Science Springer Journals

Joint Modeling of Longitudinal CD4 Count and Weight Measurements of HIV/Tuberculosis Co-infected Patients at Jimma University Specialized Hospital

Temesgen, Aboma; Kebede, Teshome
Annals of Data Science , Volume 3 (3) – Aug 20, 2016
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18 pages

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Publisher
Springer Journals
Copyright
Copyright © 2016 by The Author(s)
Subject
Business and Management; Business and Management, general; Statistics for Business/Economics/Mathematical Finance/Insurance; Computing Methodologies
ISSN
2198-5804
eISSN
2198-5812
DOI
10.1007/s40745-016-0085-9
Publisher site
See Article on Publisher Site

Abstract

As HIV/TB co-infected patients are started to be visited, it is common to measure weight and CD4 repeatedly overtime to determine the health status of patients. Most of the time linear mixed modeling of weight and CD4 count cannot handle the association between the outcomes whereas the joint modeling of multivariate linear mixed model does. Thus, this study was an attempt to model jointly the longitudinal CD4 and weight measurements of HIV/TB co-infected patients. This retrospective study consists of 254 HIV/TB co-infected patients who were 18 years old and above, and on ART followup from 1st February 2009 to 1st July 2014 at Jimma University Specialized Hospital. Firstly, weight and square root of CD4 count were analyzed separately. Based on the separate model, the joint models were built to know the correlation between mean change of weight and CD4 count overtime. Finally, appropriate model was selected based on AIC and BIC values. The fit statistics showed that the joint model fitted the data better than the separate model. From the joint model sex, educational level and functional status were the factors contributing to the prediction of HIV/TB co-infected patients weight at baseline. Beside the linear time effect has a positive effect on the mean change of weight whereas the quadratic time change has negative effect. The baseline CD4 count was differ by patient status and functional status. Further, the linear time effect has a positive sign and found to be statistically significant at 5 % level of significance on the mean change of the square root of CD4 count. Nevertheless, the quadratic time effect has a significant negative effect. The finding of the current study revealed that there was a moderate positive association between the mean change of weight and square root of CD4 count overtime.

Journal

Annals of Data Science – Springer Journals

Published: Aug 20, 2016

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