• Journal of the Korean Statistical Society
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• v.36 no.1
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• pp.129-148
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• 2007

Frailty estimates from the proportional hazards frailty model often lead us to conjecture the heterogeneity in frailty such that the variance of the frailty varies over different covariate groups (e.g. male group versus female group). For such systematic heterogeneity in frailty, we consider a regression model for the variance components in the proportional hazards frailty model, denoted by the MLFM. However, in many cases, the observed data do not show any statistically significant preference between the homogeneous frailty model and the heterogeneous frailty model. In this paper, we propose a Bayesian model averaging procedure with the reversible jump Markov chain Monte Carlo which selects the appropriate model automatically. The resulting regression coefficient estimate ignores the model uncertainty from the frailty distribution in view of Bayesian model averaging (Hoeting et al., 1999). Finally, the proposed model and the estimation procedure are illustrated through the analysis of the kidney infection data in McGilchrist and Aisbett (1991) and a simulation study is implemented.

• Journal of the Korean Data and Information Science Society
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• v.27 no.5
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• pp.1389-1397
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• 2016

Semi-parametric frailty model with nonparametric baseline hazards has been widely used for the analyses of clustered survival-time data. The frailty models can be fitted via an auxiliary Poisson hierarchical generalized linear model (HGLM). For the inferences of the frailty model marginal likelihood, which gives MLE, is often used. The marginal likelihood is usually obtained by integrating out random effects, but it often requires an intractable integration. In this paper, we propose to obtain the MLE via Laplace approximation using a Poisson HGLM approach for semi-parametric frailty model. The proposed HGLM approach uses hierarchical-likelihood (h-likelihood), which avoids integration itself. The proposed method is illustrated using a numerical study.

• Journal of Korean Society for Quality Management
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• v.26 no.4
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• pp.111-123
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• 1998

We consider the problem of modeling count data where the observation period is determined by the life time of the system under study. We assume random effects or a frailty model to allow for a possible association between the death times and the counts. We assume that, given a random effect or a frailty, the death times follow a Weibull distribution with a hazard rate. For the counts, given a frailty, a Poisson process is assumed with the intensity depending on time. A gamma distribution is assumed for the frailty model. Maximum likelihood estimators of the model parameters are obtained. A model for the time to death and the number of failures system received is constructed and consequences of the model are examined.

• Communications for Statistical Applications and Methods
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• v.25 no.5
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• pp.471-488
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• 2018

In this paper, we propose extending proportional hazards frailty models to allow a discrete distribution for the frailty variable. Having zero frailty can be interpreted as being immune or cured. Thus, we develop a new survival model induced by discrete frailty with zero-inflated power series distribution, which can account for overdispersion. This proposal also allows for a realistic description of non-risk individuals, since individuals cured due to intrinsic factors (immunes) are modeled by a deterministic fraction of zero-risk while those cured due to an intervention are modeled by a random fraction. We put the proposed model in a Bayesian framework and use a Markov chain Monte Carlo algorithm for the computation of posterior distribution. A simulation study is conducted to assess the proposed model and the computation algorithm. We also discuss model selection based on pseudo-Bayes factors as well as developing case influence diagnostics for the joint posterior distribution through ${\psi}-divergence$ measures. The motivating cutaneous melanoma data is analyzed for illustration purposes.

• The Korean Journal of Applied Statistics
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• v.18 no.1
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• pp.67-81
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• 2005

Most of the previously proposed methods for the frailty model do not work well when there are many tied observations. This is partly because the empirical likelihood used is not suitable for tied observations. In this paper, we propose a new method for the frailty model with many ties. The proposed method obtains the posterior distribution of the parameters using the binomial form empirical likelihood and Bayesian bootstrap. The proposed method yields stable results and is computationally fast. To compare the proposed method with the maximum marginal likelihood approach, we do simulations.

• Journal of the Korean Data and Information Science Society
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• v.9 no.2
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• pp.311-322
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• 1998

In this paper the problem of modeling count data where the observation period is determined by the survival time of the individual under study is considered. We assume marginal frailty model in the counts. We assume that the death times follow a Weibull distribution with a rate that depends on some covariates. For the counts, given a frailty, a Poisson process is assumed with the intensity depending on time and the covariates. A gamma model is assumed for the frailty. Maximum likelihood estimators of the model parameters are obtained. The model is applied to data set of patients with breast cancer who received a bone marrow transplant. A model for the time to death and the number of supportive transfusions a patient received is constructed and consequences of the model are examined.

• Journal of Home Health Care Nursing
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• v.26 no.1
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• pp.5-18
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• 2019

Purpose: Frailty is associated with an increased risk of adverse health outcomes. We aimed to review the relationships between frailty and health outcomes in community-dwelling Korean elderly individuals. Methods: Whittemore and Knafls' framework for conducting integrative reviews was used. PubMed, Cumulative Index to Nursing and Allied Health Literature, and six Korean databases were searched. For analysis, the study included articles written in English and Korean published between January 1960 and June 2018. Of the total 1,488 studies found in the databases, we analyzed 15 studies that met the quality of the evaluation criteria. Results: The prevalence of frailty in Korean elderly individuals ranged from 6.5% to 11.7% when divided into three levels of frailty. The health outcomes assessed in relation to frailty were divided into five domains: quality of life, physical health, psychosocial health, health behavior, and health care quality. Frailty was negatively associated with all five domains. Conclusions: Our study suggested that nurses should be aware of the limitations in the physical and cognitive functions of frail elderly individuals and provide tailored interventions for Korean elderly individuals. Furthermore, a large-scale study is needed to develop the Korean model of the frailty assessment tool and to verify the conceptual model of this study.

• The Korean Journal of Applied Statistics
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• v.25 no.5
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• pp.707-718
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• 2012

We propose a semi-parametric model to analyze clustered and interval-censored data; in addition, we plugged-in a gamma frailty to the model to measure the association of members within the same cluster. We propose an estimation procedure based on EM algorithm. Simulation results showed that our estimation procedure may result in unbiased estimates. The standard error is smaller than expected and provides conservative results to estimate the coverage rate; however, this trend gradually disappeared as the number of members in the same cluster increased. In addition, our proposed method was illustrated with data taken from diabetic retinopathy studies to evaluate the effectiveness of laser photocoagulation in delaying or preventing the onset of blindness in individuals with diabetic retinopathy.

• Journal of the Korean Statistical Society
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• v.31 no.4
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• pp.509-518
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• 2002

This paper is concerned with semiparametric Bayesian analysis of the proportional intensity regression model of the Poisson process for multiple event time data. A nonparametric prior distribution is put on the baseline cumulative intensity function and a usual parametric prior distribution is given to the regression parameter. Also we allow heterogeneity among the intensity processes in different subjects by using unobserved random frailty components. Gibbs sampling approach with the Metropolis-Hastings algorithm is used to explore the posterior distributions. Finally, the results are applied to a real data set.

• Journal of the Korean Data and Information Science Society
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• v.16 no.4
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• pp.1017-1026
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• 2005

We consider the problem of modeling count data where the observation period is determined by the survival time of the individual under study. We assume random effects or frailty model to allow for a possible association between the death times and the counts. We assume that, given a random effect, the death times follow a Weibull distribution with a rate that depends on some covariates. For the counts, given the random effect, a Poisson process is assumed with the intensity depending on time and the covariates. A gamma model is assumed for the random effect. Maximum likelihood estimators of the model parameters are obtained. The model is applied to data set of patients with breast cancer who received a bone marrow transplant. A model for the time to death and the number of supportive transfusions a patient received is constructed and consequences of the model are examined.