Joint modelling of longitudinal and time-to-event data: an illustration using CD4 count and mortality in a cohort of patients initiated on antiretroviral therapy

Mchunu, Nobuhle; Mwambi, Henry; Reddy, Tarylee; Yende‐Zuma, Nonhlanhla; Naidoo, Kogieleum

doi:10.1186/s12879-020-04962-3

Cited by 11 publications

(6 citation statements)

References 30 publications

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“…In addition, compared to the time-varying Cox model, our joint model produced smaller standard errors, indicating an increased efficiency in the joint model [ 41 ]. These results are consistent with previous research [ 42 , 43 ].…”

Section: Discussionsupporting

confidence: 94%

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

Mchunu

Mwambi

Rizopoulos

et al. 2022

BMC Med Res Methodol

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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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Section: Discussionsupporting

confidence: 94%

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

Mchunu

Mwambi

Rizopoulos

et al. 2022

BMC Med Res Methodol

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“…The proposed mixed-effects MZINB and MZIDW regression models can be further extended to investigate the association between biphasic longitudinal counts and time-to-event outcomes, 42 for example, in the context of TB trials, performing biomarker analyses to assess the association between CFU counts and "time to sputum culture conversion." 25,43 The quantile function of the discrete Weibull distribution is available in closed form, making it possible to model quantiles of counts other than the median.…”

Section: Conflict Of Interestmentioning

confidence: 99%

Nonlinear mixed‐effects modeling of longitudinal count data: Bayesian inference about median counts based on the marginal zero‐inflated discrete Weibull distribution

Burger

Lesaffre

2021

Statistics in Medicine

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This article proposes a Bayesian regression model for nonlinear zero-inflated longitudinal count data that models the median count as an alternative to the mean count. The nonlinear model generalizes a recently introduced linear mixed-effects model based on the zero-inflated discrete Weibull (ZIDW) distribution. The ZIDW distribution is more robust to severe skewness in the data than conventional zero-inflated count distributions such as the zero-inflated negative binomial (ZINB) distribution. Moreover, the ZIDW distribution is attractive because of its convenience to model the median counts given its closed-form quantile function. The median is a more robust measure of central tendency than the mean when the data, for instance, zero-inflated counts, are right-skewed.In an application of the model we consider a biphasic mixed-effects model consisting of an intercept term and two slope terms. Conventionally, the ZIDW model separately specifies the predictors for the zero-inflation probability and the counting process's median count. In our application, the two latent class interpretations are not clinically plausible. Therefore, we propose a marginal ZIDW model that directly models the biphasic median counts marginally. We also consider the marginal ZINB model to make inferences about the nonlinear mean counts over time. Our simulation study shows that the models have good properties in terms of accuracy and confidence interval coverage. K E Y W O R D SBayesian, marginal zero-inflated discrete Weibull, marginal zero-inflated negative binomial, median counts, nonlinear mixed-effects 5078

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“…Moreover, it admits that the value of a covariate at a later time point is unaffected by the event. The second argument does not apply to endogenous variables (clinical biomarkers) since a frequently observed marker such as albumin is directly connected to the mortality pathway [ 24 , 25 ].…”

Section: Discussionmentioning

confidence: 99%

Serum Albumin Trends in Relation With Prognosis of Individuals Receiving Hemodialysis Therapy

Boz¹,

Uludag²

2021

Cureus

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IntroductionHypoalbuminemia is recognized as an indication of protein-energy depletion in several disease states. According to many studies, hemodialysis (HD) patients who have decreased baseline serum albumin levels exhibit a poor prognosis. However, serum albumin does not stay at a constant level with the progress of the disease, considering that only a baseline value may not precisely reflect prognostic value. The study objective was to ascertain whether there is a link between serum albumin trajectories and all-cause mortality in incident HD patients. MethodsRetrospective cohort analysis was conducted in the HD unit at the

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Joint modelling of longitudinal and time-to-event data: an illustration using CD4 count and mortality in a cohort of patients initiated on antiretroviral therapy

Cited by 11 publications

References 30 publications

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

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

Nonlinear mixed‐effects modeling of longitudinal count data: Bayesian inference about median counts based on the marginal zero‐inflated discrete Weibull distribution

Serum Albumin Trends in Relation With Prognosis of Individuals Receiving Hemodialysis Therapy

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