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Using joint models to study the association between CD4 count and the risk of death in TB/HIV data

BACKGROUND: The association structure linking the longitudinal and survival sub-models is of fundamental importance in the joint modeling framework and the choice of this structure should be made based on the clinical background of the study. However, this information may not always be accessible an...

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Autores principales: Mchunu, Nobuhle N., Mwambi, Henry G., Rizopoulos, Dimitris, Reddy, Tarylee, Yende-Zuma, Nonhlanhla
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9675185/
https://www.ncbi.nlm.nih.gov/pubmed/36401214
http://dx.doi.org/10.1186/s12874-022-01775-7
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author Mchunu, Nobuhle N.
Mwambi, Henry G.
Rizopoulos, Dimitris
Reddy, Tarylee
Yende-Zuma, Nonhlanhla
author_facet Mchunu, Nobuhle N.
Mwambi, Henry G.
Rizopoulos, Dimitris
Reddy, Tarylee
Yende-Zuma, Nonhlanhla
author_sort Mchunu, Nobuhle N.
collection PubMed
description BACKGROUND: The association structure linking the longitudinal and survival sub-models is of fundamental importance in the joint modeling framework and the choice of this structure should be made based on the clinical background of the study. However, this information may not always be accessible and rationale for selecting this association structure has received relatively little attention in the literature. To this end, we aim to explore four alternative functional forms of the association structure between the CD4 count and the risk of death and provide rationale for selecting the optimal association structure for our data. We also aim to compare the results obtained from the joint model to those obtained from the time-varying Cox model. METHODS: We used data from the Centre for the AIDS Programme of Research in South Africa (CAPRISA) AIDS Treatment programme, the Starting Antiretroviral Therapy at Three Points in Tuberculosis (SAPiT) study, an open-label, three armed randomised, controlled trial between June 2005 and July 2010 (N=642). In our analysis, we combined the early and late integrated arms and compared results to the sequential arm. We utilized the Deviance Information Criterion (DIC) to select the final model with the best structure, with smaller values indicating better model adjustments to the data. RESULTS: Patient characteristics were similar across the study arms. Combined integrated therapy arms had a reduction of 55% in mortality (HR:0.45, 95% CI:0.28-0.72) compared to the sequential therapy arm. The joint model with a cumulative effects functional form was chosen as the best association structure. In particular, our joint model found that the area under the longitudinal profile of CD4 count was strongly associated with a 21% reduction in mortality (HR:0.79, 95% CI:0.72-0.86). Where as results from the time-varying Cox model showed a 19% reduction in mortality (HR:0.81, 95% CI:0.77-0.84). CONCLUSIONS: In this paper we have shown that the “current value” association structure is not always the best structure that expresses the correct relationship between the outcomes in all settings, which is why it is crucial to explore alternative clinically meaningful association structures that links the longitudinal and survival processes.
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spelling pubmed-96751852022-11-20 Using joint models to study the association between CD4 count and the risk of death in TB/HIV data Mchunu, Nobuhle N. Mwambi, Henry G. Rizopoulos, Dimitris Reddy, Tarylee Yende-Zuma, Nonhlanhla BMC Med Res Methodol Research BACKGROUND: The association structure linking the longitudinal and survival sub-models is of fundamental importance in the joint modeling framework and the choice of this structure should be made based on the clinical background of the study. However, this information may not always be accessible and rationale for selecting this association structure has received relatively little attention in the literature. To this end, we aim to explore four alternative functional forms of the association structure between the CD4 count and the risk of death and provide rationale for selecting the optimal association structure for our data. We also aim to compare the results obtained from the joint model to those obtained from the time-varying Cox model. METHODS: We used data from the Centre for the AIDS Programme of Research in South Africa (CAPRISA) AIDS Treatment programme, the Starting Antiretroviral Therapy at Three Points in Tuberculosis (SAPiT) study, an open-label, three armed randomised, controlled trial between June 2005 and July 2010 (N=642). In our analysis, we combined the early and late integrated arms and compared results to the sequential arm. We utilized the Deviance Information Criterion (DIC) to select the final model with the best structure, with smaller values indicating better model adjustments to the data. RESULTS: Patient characteristics were similar across the study arms. Combined integrated therapy arms had a reduction of 55% in mortality (HR:0.45, 95% CI:0.28-0.72) compared to the sequential therapy arm. The joint model with a cumulative effects functional form was chosen as the best association structure. In particular, our joint model found that the area under the longitudinal profile of CD4 count was strongly associated with a 21% reduction in mortality (HR:0.79, 95% CI:0.72-0.86). Where as results from the time-varying Cox model showed a 19% reduction in mortality (HR:0.81, 95% CI:0.77-0.84). CONCLUSIONS: In this paper we have shown that the “current value” association structure is not always the best structure that expresses the correct relationship between the outcomes in all settings, which is why it is crucial to explore alternative clinically meaningful association structures that links the longitudinal and survival processes. BioMed Central 2022-11-18 /pmc/articles/PMC9675185/ /pubmed/36401214 http://dx.doi.org/10.1186/s12874-022-01775-7 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Mchunu, Nobuhle N.
Mwambi, Henry G.
Rizopoulos, Dimitris
Reddy, Tarylee
Yende-Zuma, Nonhlanhla
Using joint models to study the association between CD4 count and the risk of death in TB/HIV data
title Using joint models to study the association between CD4 count and the risk of death in TB/HIV data
title_full Using joint models to study the association between CD4 count and the risk of death in TB/HIV data
title_fullStr Using joint models to study the association between CD4 count and the risk of death in TB/HIV data
title_full_unstemmed Using joint models to study the association between CD4 count and the risk of death in TB/HIV data
title_short Using joint models to study the association between CD4 count and the risk of death in TB/HIV data
title_sort using joint models to study the association between cd4 count and the risk of death in tb/hiv data
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9675185/
https://www.ncbi.nlm.nih.gov/pubmed/36401214
http://dx.doi.org/10.1186/s12874-022-01775-7
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