scholarly journals A flexible joint model for multiple longitudinal biomarkers and a time‐to‐event outcome: With applications to dynamic prediction using highly correlated biomarkers

2021 ◽  
Author(s):  
Ning Li ◽  
Yi Liu ◽  
Robert M. Elashoff ◽  
Gang Li
Keyword(s):  
Joint Model ◽  
Time To Event ◽  
Dynamic Prediction ◽  
Flexible Joint ◽  
Highly Correlated

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

scholarly journals A flexible joint modeling framework for longitudinal and time-to-event data with overdispersion

2016 ◽  
Vol 25 (4) ◽  
pp. 1661-1676 ◽  
Author(s):  
Edmund N Njagi ◽  
Geert Molenberghs ◽  
Dimitris Rizopoulos ◽  
Geert Verbeke ◽  
Michael G Kenward ◽  
...  
Keyword(s):  
Joint Modeling ◽  
Event Data ◽  
Time To Event ◽  
Modeling Framework ◽  
Flexible Joint ◽  
Time To Event Data

Impact of Joint Modeling Approach on Performance Estimates of Older-Type RC Buildings

2017 ◽  
Vol 33 (3) ◽  
pp. 1101-1123 ◽  
Author(s):  
Beyhan Bayhan ◽  
Gökhan Özdemir ◽  
Polat Gülkan
Keyword(s):  
Joint Modeling ◽  
Joint Model ◽  
Analytical Models ◽  
Base Shear ◽  
Joint Rotation ◽  
Seismic Demands ◽  
Flexible Joint ◽  
Dynamic Analyses ◽  
Column Joint ◽  
Nonlinear Dynamic Analyses

The behavior of beam-column connections has usually been ignored in the modeling process due to its complexity and relatively recent awareness of its possible impact on response. This study presents the features of modeling unreinforced beam-column joints in estimating seismic demands. A representative RC frame is subjected to strong ground motions. Through nonlinear dynamic analyses, base shear, roof displacement, inter-story drift and joint rotation are noted. The dynamic analyses are performed comparatively through two analytical models with rigid and flexible joint assumptions. In the flexible joint model, shear deformation at the beam-column joint and bond-slip deformation at the beam-column interface are simulated through a previously verified analytical representation. Results indicate that introducing unreinforced beam-column joint behavior to the model may lead to almost two times larger seismic demands compared to those obtained from rigid connection assumption. Thus, the performance assessment of such buildings may conclude erroneously with underestimated seismic demands and damage levels when inelastic actions in the joints are ignored. However, in some cases, lower seismic demands can also be obtained for the flexible joint model.

SO089THE EVOLUTION OF THE RENAL FUNCTION IN A COHORT OF ITALIAN PATIENTS WITH ADPKD: AN ANALYSIS BY THE JOINT MODEL

2020 ◽  
Vol 35 (Supplement_3) ◽  
Author(s):  
Marco Galliani ◽  
Elio Vitaliano ◽  
Silvana Chicca ◽  
Antonio Paone ◽  
Luca Di Lullo ◽  
...  
Keyword(s):  
Renal Function ◽  
Cox Model ◽  
Central Italy ◽  
Age Groups ◽  
Mixed Linear Model ◽  
Joint Model ◽  
Italian Population ◽  
Time To Event ◽  
Longitudinal Model ◽  
History Of

Abstract Background and Aims The natural history of ADPKD has been defined in numerous studies in various countries and in various genetic and environmental contexts but the problem has not yet been studied in sufficiently large studies in the Italian population. We have studied the evolution of ADPKD in a cohort of 445 incident patients enrolled in a time span between 2012 and 2016 in 28 nephrological centers in 3 regions of central Italy. The main demographic and clinical characteristics of this cohort are reported below. [GRAPHICS] Method The analysis was carried out on a sample of 296 patients with at least 2 creatinine measurements prior to ESKD and with eGFR at onset ≥45 ml/min/1.73m2. The median follow-up was 15 years (range 1-42 years). The analysis of the data was carried out with the Joint model, an approach that allows the simultaneous modeling of longitudinal (repeated over time) and time to event ESKD or GFR<45 ml/min/1.73m2 data. This approach produces two sub-models: a longitudinal model (mixed linear model, sub-model A in the table) and a time to event model (Cox model, survival sub-model B in the Table). The two models are mutually associated through an analytical structure that quantifies the relationship between the variables of interest. [GRAPHICS] [GRAPHICS] Results The average eGFR decline in the entire population was -2.13 ml/min/1.73m2/year. In males the decline in GFR was significantly higher than in females (M: -2.63 ml/min/1.73m2; in F -1.74 ml/min/1.73m2/year, p <0.005) while it was not significant differences are observed for the different age groups considered separately in the two sexes (p = 0.158 in the M; p = 0.625 in the F). In the longitudinal model the age at diagnosis >30 years and the presence of hypertension were significantly associated with an accelerated decline in GFR and patients older than 50 years exhibited a reduction by 35 ml/min/1.73m2 greater than those younger than 30 years. In the survival model the GFR trend and sex were associated with the risk of renal survival. For each incremental baseline unit of GFR the risk for ESKD decreased by 16% (HR: 0.84) and in females the risk was about one third compared to males (HR: 0.31). Conclusion This study, the first to apply the joint model in the analysis of the evolution of renal function in ADPKD and the first in a sizeable cohort in Italy, provides important information on the progression of polycystic kidney disease in an incident series of Italian patients with ADPKD.

Bayesian variable selection and shrinkage strategies in a complicated modelling setting with missing data: A case study using multistate models

2020 ◽  
pp. 1471082X2092097
Author(s):  
Lauren J Beesley ◽  
Jeremy MG Taylor
Keyword(s):  
Missing Data ◽  
Variable Selection ◽  
Bayesian Variable Selection ◽  
Multistate Models ◽  
Time To Event ◽  
Shrinkage Methods ◽  
Highly Correlated ◽  
Correlated Predictors ◽  
Insight Into

Multistate modelling is a strategy for jointly modelling related time-to-event outcomes that can handle complicated outcome relationships, has appealing interpretations, can provide insight into different aspects of disease development and can be useful for making individualized predictions. A challenge with using multistate modelling in practice is the large number of parameters, and variable selection and shrinkage strategies are needed in order for these models to gain wider adoption. Application of existing selection and shrinkage strategies in the multistate modelling setting can be challenging due to complicated patterns of data missingness, inclusion of highly correlated predictors and hierarchical parameter relationships. In this article, we discuss how to modify and implement several existing Bayesian variable selection and shrinkage methods in a general multistate modelling setting. We compare the performance of these methods in terms of parameter estimation and model selection in a multistate cure model of recurrence and death in patients treated for head and neck cancer. We can view this work as a case study of variable selection and shrinkage in a complicated modelling setting with missing data.

A joint model for longitudinal continuous and time-to-event outcomes with direct marginal interpretation

2013 ◽  
Vol 55 (4) ◽  
pp. 572-588 ◽  
Author(s):  
Achmad Efendi ◽  
Geert Molenberghs ◽  
Edmund Njeru Njagi ◽  
Paul Dendale
Keyword(s):  
Joint Model ◽  
Time To Event
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