A novel method for joint modeling of survival data and count data for both simple randomized and cluster randomized data

2020 ◽  
pp. 1-23
Author(s):  
A. A. Sunethra ◽  
M. R. Sooriyarachchi
Keyword(s):  
Survival Data ◽  
Count Data ◽  
Joint Modeling ◽  
Novel Method ◽  
Cluster Randomized

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

scholarly journals Quantification of the Relationship between Bacterial Kinetics and Host Response for Monkeys Exposed to AerosolizedFrancisella tularensis

2010 ◽  
Vol 77 (2) ◽  
pp. 485-490 ◽  
Author(s):  
Yin Huang ◽  
Charles N. Haas
Keyword(s):  
Survival Data ◽  
Count Data ◽  
Host Response ◽  
Growth Curves ◽  
Bacterial Count ◽  
Extensive Literature ◽  
Schu S4 ◽  
The Relationship ◽  
Dose Dependent

ABSTRACTFrancisella tularensiscan be disseminated via aerosols, and once inhaled, only a few microorganisms may result in tularemia pneumonia. Effective responses to this threat depend on a thorough understanding of the disease development and pathogenesis. In this study, a class of time-dose-response models was expanded to describe quantitatively the relationship between the temporal probability distribution of the host response and thein vivobacterial kinetics. An extensive literature search was conducted to locate both the dose-dependent survival data and thein vivobacterial count data of monkeys exposed to aerosolizedF. tularensis. One study reporting responses of monkeys to four different sizes of aerosol particles (2.1, 7.5, 12.5, and 24.0 μm) of the SCHU S4 strain and three studies involving fivein vivogrowth curves of various strains (SCHU S4, 425, and live vaccine strains) initially delivered to hosts in aerosol form (1 to 5 μm) were found. The candidate models exhibited statistically acceptable fits to the time- and dose-dependent host response and provided estimates for the bacterial growth distribution. The variation pattern of such estimates with aerosol size was found to be consistent with the reported pathophysiological and clinical observations. The predicted growth curve for 2.1-μm aerosolized bacteria was highly consistent with the available bacterial count data. This is the first instance in which the relationship between thein vivogrowth ofF.tularensisand the host response can be quantified by mechanistic mathematical models.

scholarly journals Joint Modeling of Longitudinal and Cure-Survival Data

2013 ◽  
Vol 7 (2) ◽  
pp. 324-344 ◽  
Author(s):  
Sehee Kim ◽  
Donglin Zeng ◽  
Yi Li ◽  
Donna Spiegelman
Keyword(s):  
Survival Data ◽  
Joint Modeling
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