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

• Published in: BMC medical research methodology Vol. 22; no. 1; pp. 1 - 295
• Main Authors: Mchunu, Nobuhle N, Mwambi, Henry G, Rizopoulos, Dimitris, Reddy, Tarylee, Yende-Zuma, Nonhlanhla
• Format: Journal Article
• Language: English
• Published: London BioMed Central Ltd 18-11-2022; BioMed Central; BMC
• Subjects: Acquired immune deficiency syndrome; AIDS; Antiretroviral drugs; Association structures; Biomarkers; CD4 count; CD4 lymphocytes; Gender; Health aspects; HIV; HIV infection; Human immunodeficiency virus; Joint models; Longitudinal data; Measurement; Medical research; Mortality; Patient outcomes; Research methodology; Risk factors; Time-to-event data; Tuberculosis; South Africa
• ISSN: 1471-2288; 1471-2288
• DOI: 10.1186/s12874-022-01775-7

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. Keywords: Time-to-event data, Longitudinal data, Joint models, CD4 count, Mortality, Association structures