Comments on ‘Joint modeling of survival and longitudinal non-survival data: current methods and issues. Report of the DIA Bayesian Joint Modeling Working Group’

2015 ◽  
Vol 34 (14) ◽  
pp. 2196-2197 ◽  
Author(s):  
Dimitris Rizopoulos
Keyword(s):  
Survival Data ◽  
Working Group ◽  
Joint Modeling

scholarly journals Responses to discussants of ‘Joint modeling of survival and longitudinal non-survival data: current methods and issues. report of the DIA Bayesian joint modeling working group’

2015 ◽  
Vol 34 (14) ◽  
pp. 2202-2203 ◽  
Author(s):  
A. Lawrence Gould ◽  
Mark Ernest Boye ◽  
Michael J. Crowther ◽  
Joseph G. Ibrahim ◽  
George Quartey ◽  
...  
Keyword(s):  
Survival Data ◽  
Working Group ◽  
Joint Modeling

scholarly journals Joint modeling of survival and longitudinal non-survival data: current methods and issues. Report of the DIA Bayesian joint modeling working group

2014 ◽  
Vol 34 (14) ◽  
pp. 2181-2195 ◽  
Author(s):  
A. Lawrence Gould ◽  
Mark Ernest Boye ◽  
Michael J. Crowther ◽  
Joseph G. Ibrahim ◽  
George Quartey ◽  
...  
Keyword(s):  
Survival Data ◽  
Working Group ◽  
Joint Modeling

scholarly journals Joint modeling of multiple repeated measures and survival data using multidimensional latent trait linear mixed model

2018 ◽  
Vol 28 (10-11) ◽  
pp. 3392-3403 ◽  
Author(s):  
Jue Wang ◽  
Sheng Luo
Keyword(s):  
Survival Data ◽  
Repeated Measures ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Proportional Hazards Model ◽  
Latent Trait ◽  
Joint Modeling ◽  
Fine Motor ◽  
Terminal Event ◽  
Longitudinal Outcomes

Impairment caused by Amyotrophic lateral sclerosis (ALS) is multidimensional (e.g. bulbar, fine motor, gross motor) and progressive. Its multidimensional nature precludes a single outcome to measure disease progression. Clinical trials of ALS use multiple longitudinal outcomes to assess the treatment effects on overall improvement. A terminal event such as death or dropout can stop the follow-up process. Moreover, the time to the terminal event may be dependent on the multivariate longitudinal measurements. In this article, we develop a joint model consisting of a multidimensional latent trait linear mixed model (MLTLMM) for the multiple longitudinal outcomes, and a proportional hazards model with piecewise constant baseline hazard for the event time data. Shared random effects are used to link together two models. The model inference is conducted using a Bayesian framework via Markov chain Monte Carlo simulation implemented in Stan language. Our proposed model is evaluated by simulation studies and is applied to the Ceftriaxone study, a motivating clinical trial assessing the effect of ceftriaxone on ALS patients.

scholarly journals Physical Functioning Decline and Mortality in Older Adults With Multimorbidity: Joint Modeling of Longitudinal and Survival Data

2018 ◽  
Vol 74 (2) ◽  
pp. 226-232 ◽  
Author(s):  
Melissa Y Wei ◽  
Mohammed U Kabeto ◽  
Andrzej T Galecki ◽  
Kenneth M Langa
Keyword(s):  
Older Adults ◽  
Mortality Risk ◽  
Physical Functioning ◽  
Survival Data ◽  
Short Form ◽  
Joint Modeling ◽  
Full Spectrum ◽  
Increased Risk ◽  
The Impact

Abstract Background Multimorbidity is common among older adults and strongly associated with physical functioning decline and increased mortality. However, the full spectrum of direct and indirect effects of multimorbidity on physical functioning and survival has not been quantified. We aimed to determine the longitudinal relationship of multimorbidity on physical functioning and quantify the impact of multimorbidity and multimorbidity-attributed changes in physical functioning on mortality risk. Methods The Health and Retirement Study (HRS) is a nationally representative population-based prospective cohort of adults aged 51 or older. In 2000, participants were interviewed about physician-diagnosed chronic conditions, from which their multimorbidity-weighted index (MWI) was computed. Between 2000 and 2011, participants reported their current physical functioning using a modified Short Form-36. With MWI as a time-varying exposure, we jointly modeled its associations with physical functioning and survival. Results The final sample included 74,037 observations from 18,174 participants. At baseline, participants had a weighted mean MWI of 4.6 ± 4.2 (range 0–36.8). During follow-up, physical functioning declined: −1.72 (95% confidence interval [CI] −1.77, −1.67, p < .001) HRS physical functioning units per point MWI in adjusted models. Over follow-up, 6,362 (34%) participants died. Mortality risk increased 8% (hazard ratio 1.08, 95% CI 1.07–1.08, p < .001) per point MWI in adjusted models. Across all population subgroups, MWI was associated with greater physical functioning decline and mortality risk. Conclusions Multimorbidity and its associated decline in physical functioning were significantly associated with increased mortality. These associations can be predicted with an easily interpreted and applied multimorbidity index that can better identify and target adults at increased risk for disability and death.

scholarly journals Sample size and power determination in joint modeling of longitudinal and survival data

2011 ◽  
Vol 30 (18) ◽  
pp. 2295-2309 ◽  
Author(s):  
Liddy M. Chen ◽  
Joseph G. Ibrahim ◽  
Haitao Chu
Keyword(s):  
Sample Size ◽  
Survival Data ◽  
Joint Modeling ◽  
Longitudinal And Survival Data
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