Joint Model for Longitudinal and Time-To-Event Data: a Two-Stage Approach

2021 ◽  
Vol 10 (3) ◽  
pp. 807-819
Keyword(s):  
Joint Model ◽  
Event Data ◽  
Time To Event ◽  
Two Stage ◽  
Time To Event Data

scholarly journals 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

2020 ◽  
Author(s):  
Nobuhle Nokubonga Mchunu ◽  
Henry Mwambi ◽  
Tarylee Reddy ◽  
Nonhlanhla Yende-Zuma ◽  
Kogieleum Naidoo
Keyword(s):  
Cox Model ◽  
Joint Model ◽  
Cd4 Count ◽  
Time Varying ◽  
Event Data ◽  
Time To Event ◽  
Joint Modelling ◽  
Risk Of Death ◽  
Time To Event Data ◽  
Over Time

Abstract Background: Modelling of longitudinal biomarkers and time-to-event data are important to monitor disease progression. However, these two variables are traditionally analyzed separately or time-varying Cox models are used. The former strategy fails to recognize the shared random-effects from the two processes while the latter assumes that longitudinal biomarkers are exogenous covariates, resulting in inefficient or biased estimates for the time-to-event model. Therefore, we used joint modelling for longitudinal and time-to-event data to assess the effect of longitudinal CD4 count on mortality. Methods: We studied 4014 patients from the Centre for the AIDS Programme of Research in South Africa (CAPRISA) who initiated ART between June 2004 and August 2013. We used proportional hazards regression model to assess the effect of baseline characteristics (excluding CD4 count) on mortality, and linear mixed effect models to evaluate the effect of baseline characteristics on the CD4 count evolution over time. Thereafter, the two analytical approaches were amalgamated to form an advanced joint model for studying the effect of longitudinal CD4 count on mortality. To illustrate the virtues of the joint model, the results from the joint model were compared to those from the time-varying Cox model. Results: Using joint modelling, we found that lower CD4 count over time was associated with a 1.3-fold increase in the risk of death, (HR: 1.34, 95% CI: 1.27-1.42). Whereas, results from the time-varying Cox model showed lower CD4 count over time was associated with a 1.2-fold increase in the risk of death, (HR: 1.17, 95% CI: 1.12-1.23). Conclusions: Joint modelling enabled the assessment of the effect of longitudinal CD4 count on mortality while correcting for shared random effects between longitudinal and time-to-event models. In the era of universal test and treat, the evaluation of CD4 count is still crucial for guiding the initiation and discontinuation of opportunistic infections prophylaxis and assessment of late presenting patients. CD4 count can also be used when immunological failure is suspected as we have shown that it is associated with mortality. Keywords: Time-to-event data; longitudinal data; joint models; CD4 count; mortality; bias

Meta-analysis of time-to-event data: a comparison of two-stage methods

2011 ◽  
Vol 2 (3) ◽  
pp. 139-149 ◽  
Author(s):  
Mark C. Simmonds ◽  
Jayne Tierney ◽  
Jack Bowden ◽  
Julian PT Higgins
Keyword(s):  
Meta Analysis ◽  
Event Data ◽  
Time To Event ◽  
Two Stage ◽  
Time To Event Data

scholarly journals Point and interval estimation in two‐stage adaptive designs with time to event data and biomarker‐driven subpopulation selection

2020 ◽  
Vol 39 (19) ◽  
pp. 2568-2586
Author(s):  
Peter K. Kimani ◽  
Susan Todd ◽  
Lindsay A. Renfro ◽  
Ekkehard Glimm ◽  
Josephine N. Khan ◽  
...  
Keyword(s):  
Interval Estimation ◽  
Adaptive Designs ◽  
Event Data ◽  
Time To Event ◽  
Two Stage ◽  
Time To Event Data

An Overview of Joint Modeling of Time-to-Event and Longitudinal Outcomes

2019 ◽  
Vol 6 (1) ◽  
pp. 223-240 ◽  
Author(s):  
Grigorios Papageorgiou ◽  
Katya Mauff ◽  
Anirudh Tomer ◽  
Dimitris Rizopoulos
Keyword(s):  
Joint Modeling ◽  
Joint Model ◽  
Event Data ◽  
Time To Event ◽  
Joint Models ◽  
Longitudinal Outcomes ◽  
Estimation Techniques ◽  
Time To Event Data ◽  
Association Structure

In this review, we present an overview of joint models for longitudinal and time-to-event data. We introduce a generalized formulation for the joint model that incorporates multiple longitudinal outcomes of varying types. We focus on extensions for the parametrization of the association structure that links the longitudinal and time-to-event outcomes, estimation techniques, and dynamic predictions. We also outline the software available for the application of these models.

scholarly journals 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

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Nobuhle N. Mchunu ◽  
Henry G. Mwambi ◽  
Tarylee Reddy ◽  
Nonhlanhla Yende-Zuma ◽  
Kogieleum Naidoo
Keyword(s):  
Cox Model ◽  
Joint Model ◽  
Cd4 Count ◽  
Time Varying ◽  
Event Data ◽  
Time To Event ◽  
Joint Modelling ◽  
Risk Of Death ◽  
Time To Event Data ◽  
Over Time

Abstract Background Modelling of longitudinal biomarkers and time-to-event data are important to monitor disease progression. However, these two variables are traditionally analyzed separately or time-varying Cox models are used. The former strategy fails to recognize the shared random-effects from the two processes while the latter assumes that longitudinal biomarkers are exogenous covariates, resulting in inefficient or biased estimates for the time-to-event model. Therefore, we used joint modelling for longitudinal and time-to-event data to assess the effect of longitudinal CD4 count on mortality. Methods We studied 4014 patients from the Centre for the AIDS Programme of Research in South Africa (CAPRISA) who initiated ART between June 2004 and August 2013. We used proportional hazards regression model to assess the effect of baseline characteristics (excluding CD4 count) on mortality, and linear mixed effect models to evaluate the effect of baseline characteristics on the CD4 count evolution over time. Thereafter, the two analytical approaches were amalgamated to form an advanced joint model for studying the effect of longitudinal CD4 count on mortality. To illustrate the virtues of the joint model, the results from the joint model were compared to those from the time-varying Cox model. Results Using joint modelling, we found that lower CD4 count over time was associated with a 1.3-fold increase in the risk of death, (HR: 1.34, 95% CI: 1.27-1.42). Whereas, results from the time-varying Cox model showed lower CD4 count over time was associated with a 1.2-fold increase in the risk of death, (HR: 1.17, 95% CI: 1.12-1.23). Conclusions Joint modelling enabled the assessment of the effect of longitudinal CD4 count on mortality while correcting for shared random effects between longitudinal and time-to-event models. In the era of universal test and treat, the evaluation of CD4 count is still crucial for guiding the initiation and discontinuation of opportunistic infections prophylaxis and assessment of late presenting patients. CD4 count can also be used when immunological failure is suspected as we have shown that it is associated with mortality.

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