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 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

2017 ◽  
Vol 47 (3) ◽  
pp. 681-713 ◽  
Author(s):  
Frank van Berkum ◽  
Katrien Antonio ◽  
Michel Vellekoop
Keyword(s):  
Parameter Uncertainty ◽  
Mortality Rates ◽  
Insurance Companies ◽  
Posterior Distributions ◽  
Short Term ◽  
Specific Mortality ◽  
Extensive Information ◽  
Specific Factors ◽  
Population Mortality ◽  
Bayesian Joint Model

AbstractInsurance companies and pension funds must value liabilities using mortality rates that are appropriate for their portfolio. These can only be estimated in a reliable way from a sufficiently large historical dataset for such portfolios, which is often not available. We overcome this problem by introducing a model to estimate portfolio-specific mortality simultaneously with population mortality. By using a Bayesian framework, we automatically generate the appropriate weighting for the limited statistical information in a given portfolio and the more extensive information that is available for the whole population. This allows us to separate parameter uncertainty from uncertainty due to the randomness in individual deaths for a given realization of mortality rates. When we apply our method to a dataset of assured lives in England and Wales, we find that different prior specifications for the portfolio-specific factors lead to significantly different posterior distributions for hazard rates. However, in short-term predictive distributions for future numbers of deaths, individual mortality risk turns out to be more important than parameter uncertainty in the portfolio-specific factors, both for large and for small portfolios.

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 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 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
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