scholarly journals Evaluation of Factors Related to Survival Time in HIV-Infected Persons in Mashhad, Iran, Between 1994 - 2014: A Bayesian Joint Model

2019 ◽  
Vol 6 (3) ◽  
Author(s):  
Niloofar Shabani ◽  
Habibollah Esmaily ◽  
Rasul Alimi ◽  
Abdolhamid Rezaei Roknabadi
Keyword(s):  
Survival Time ◽  
Joint Model ◽  
Bayesian Joint Model

scholarly journals A Bayesian joint model for zero‐inflated integers and left‐truncated event times with a time‐varying association: Applications to senior health care

2020 ◽  
Vol 40 (1) ◽  
pp. 147-166
Author(s):  
Xavier Piulachs ◽  
Eleni‐Rosalina Andrinopoulou ◽  
Montserrat Guillén ◽  
Dimitris Rizopoulos
Keyword(s):  
Health Care ◽  
Joint Model ◽  
Time Varying ◽  
Event Times ◽  
Bayesian Joint Model ◽  
Senior Health Care ◽  
Senior Health

scholarly journals A new Bayesian joint model for longitudinal count data with many zeros, intermittent missingness, and dropout with applications to HIV prevention trials

2019 ◽  
Vol 38 (30) ◽  
pp. 5565-5586
Author(s):  
Jing Wu ◽  
Ming‐Hui Chen ◽  
Elizabeth D. Schifano ◽  
Joseph G. Ibrahim ◽  
Jeffrey D. Fisher
Keyword(s):  
Hiv Prevention ◽  
Count Data ◽  
Joint Model ◽  
Longitudinal Count Data ◽  
Prevention Trials ◽  
Bayesian Joint Model

scholarly journals A Bayesian Joint Model for Population and Portfolio-Specific Mortality

2015 ◽  
Author(s):  
Frank van Berkum ◽  
Katrien Antonio ◽  
Michel H. Vellekoop
Keyword(s):  
Joint Model ◽  
Specific Mortality ◽  
Bayesian Joint Model

scholarly journals Determinants of Intraocular Pressure and Time to Blindness for Glaucoma Patients at Felege Hiwot Referral Hospital, Bahir Bar, Ethiopia: A Comparison of Separate and Joint Models

2021 ◽  
Vol 20 ◽  
pp. 117693512110459
Author(s):  
Mitiku Wale Muluneh ◽  
Awoke Seyoum Tegegne
Keyword(s):  
Intraocular Pressure ◽  
Survival Time ◽  
Joint Model ◽  
Referral Hospital ◽  
Public Health Issue ◽  
P Value ◽  
Ratio Test ◽  
Number Of Patients ◽  
Felege Hiwot Referral Hospital

Background: Due to the substantial increase in the number of glaucoma cases within the next several decades, glaucoma is a significant public health issue. The main objective of this study was to investigate the determinant factors of intraocular pressure and time to blindness of glaucoma patients under treatment at Felege Hiwot Referral Hospital, Bahir Dar, Ethiopia. Methods: A retrospective study design was conducted on 328 randomly selected glaucoma patients using simple random sampling based on the identification number of patients in an ophthalmology clinic at the hospital under the follow-up period from January 2014 to December 2018. A linear mixed effects model for intraocular pressure data, a semi-parametric survival model for the time-to-blindness data and joint modeling of the 2 responses were used for data analysis. However, the primary outcome was survival time of glaucoma patients. Results: The comparison of joint and separate models revealed that joint model was more adequate and efficient inferences because of its smaller standard errors in parameter estimations. This was also approved using AIC, BIC, and based on a significant likelihood ratio test as well. The estimated association parameter (α) in the joint model was .0160 and statistically significant ( P-value = .0349). This indicated that there was strong evidence for positive association between the effects of intraocular pressure and the risk of blindness. The result indicated that the higher value of intraocular pressure was associated with the higher risk of blindness. Age, hypertension, type of medication, cup-disk ratio significantly affects both average intraocular pressure and survival time of glaucoma patients ( P-value < .05). Conclusion: The predictors; age, hypertension, type of medication, and cup-disk ratio were significantly associated with the 2 responses of glaucoma patients. Health professionals give more attention to patients who have blood pressure and cup-disk ratio greater than 0.7 during the follow-up time to reduce the risk of blindness of glaucoma patients.

Two-part joint model for a longitudinal semicontinuous marker and a terminal event with application to metastatic colorectal cancer data

Biostatistics ◽  
2020 ◽  
Author(s):  
Denis Rustand ◽  
Laurent Briollais ◽  
Christophe Tournigand ◽  
Virginie Rondeau
Keyword(s):  
Colorectal Cancer ◽  
Metastatic Colorectal Cancer ◽  
Survival Time ◽  
Joint Model ◽  
Terminal Event ◽  
Joint Models ◽  
Cancer Data ◽  
Longitudinal Biomarker ◽  
Part Model ◽  
Over Time

Summary Joint models for a longitudinal biomarker and a terminal event have gained interests for evaluating cancer clinical trials because the tumor evolution reflects directly the state of the disease. A biomarker characterizing the tumor size evolution over time can be highly informative for assessing treatment options and could be taken into account in addition to the survival time. The biomarker often has a semicontinuous distribution, i.e., it is zero inflated and right skewed. An appropriate model is needed for the longitudinal biomarker as well as an association structure with the survival outcome. In this article, we propose a joint model for a longitudinal semicontinuous biomarker and a survival time. The semicontinuous nature of the longitudinal biomarker is specified by a two-part model, which splits its distribution into a binary outcome (first part) represented by the positive versus zero values and a continuous outcome (second part) with the positive values only. Survival times are modeled with a proportional hazards model for which we propose three association structures with the biomarker. Our simulation studies show some bias can arise in the parameter estimates when the semicontinuous nature of the biomarker is ignored, assuming the true model is a two-part model. An application to advanced metastatic colorectal cancer data from the GERCOR study is performed where our two-part model is compared to one-part joint models. Our results show that treatment arm B (FOLFOX6/FOLFIRI) is associated to higher SLD values over time and its positive association with the terminal event leads to an increased risk of death compared to treatment arm A (FOLFIRI/FOLFOX6).

scholarly journals Estimating Health Demand for an Aging Population: A Flexible and Robust Bayesian Joint Model

2015 ◽  
Vol 31 (6) ◽  
pp. 1140-1158 ◽  
Author(s):  
Arnab Mukherji ◽  
Satrajit Roychoudhury ◽  
Pulak Ghosh ◽  
Sarah Brown
Keyword(s):  
Aging Population ◽  
Joint Model ◽  
Health Demand ◽  
Bayesian Joint Model

scholarly journals Joint Modeling of  Longitudinal Ordinal Data on  Quality of Life and Survival

2021 ◽  
Author(s):  
◽  
Kemmawadee Preedalikit
Keyword(s):  
Quality Of Life ◽  
Survival Data ◽  
Latent Variable ◽  
Ordinal Data ◽  
Likelihood Function ◽  
Joint Model ◽  
Hiv Progression ◽  
Bayesian Joint Model ◽  
Lower Hazard

<p>Joint models for longitudinal and survival data have been widely discussed in the literature. This thesis proposes a joint model using a stereotype model for the longitudinal ordinal responses and a Cox proportional hazards model for survival time. Our current joint model has a new feature since no literature has examined the joint model under the stereotype model. The stereotype model can improve the fit by adding extra score parameters, but it still has the advantage of requiring only a single parameter to describe the effect of a predictor on the item response levels. We give an example to model longitudinal ordinal data and survival data for patients being followed up after treatments. The main focus is on modeling both the quality of life data and the survival data simultaneously with a goal of understanding the association between the two processes over time. These two models are linked through a latent variable that characterizes the quality of life of an individual and is assumed to underlie the hazard rate. In other words, the latent variable serves as a shared variable in the joint model. We present the joint model in two different aspects: one based on a Bayesian approach and the other one a semiparametric approach using the EM algorithm. For the Bayesian approach, the latent variable is treated as a continuous variable and is assumed to have a multivariate normal distribution. The partial survival likelihood function is used in the survival component of the Bayesian joint model, while the full likelihood function is considered in the semiparametric joint model. In the latter approach the baseline hazard is assumed to be a step function and has no parametric form. The latent variable in the semiparametric joint model is then treated as a discrete variable. We illustrate our methodologies by analyzing data from the Staccato study, a randomized trial to compare two treatment methods, for Human Immunodeficiency Virus (HIV) infection of Thai patients on Highly Active Antiretroviral Therapy (HAART), in which the quality of life was assessed with a HIV Medical Outcome Study (MOS-HIV) questionnaire. Furthermore, we extend the study further to the case of multiple failure types in the survival component. Thus, the extension of the joint model consists of the stereotype model and the competing risks model. The Bayesian method is employed to estimate all unknown parameters in this extended joint model. The results we obtained are consistent for both the Bayesian joint model and the semiparametric joint model. Both models show that patients who had a better quality of life were associated with a lower hazard of HIV progression. Patients on continuous treatment also had a lower hazard of HIV progression compared with patients on CD4-guided interruption treatment.</p>

scholarly journals A Bayesian joint model of menstrual cycle length and fecundity

Biometrics ◽  
2015 ◽  
Vol 72 (1) ◽  
pp. 193-203 ◽  
Author(s):  
Kirsten J. Lum ◽  
Rajeshwari Sundaram ◽  
Germaine M. Buck Louis ◽  
Thomas A. Louis
Keyword(s):  
Menstrual Cycle ◽  
Cycle Length ◽  
Joint Model ◽  
Menstrual Cycle Length ◽  
Bayesian Joint Model
Sign in / Sign up

Export Citation Format

Share Document