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Joint HGLM approach for repeated measures and survival data
하일도 한국데이터정보과학회 2016 한국데이터정보과학회지 Vol.27 No.4
In clinical studies, different types of outcomes (e.g. repeated measures data and time-to-event data) for the same subject tend to be observed, and these data can be correlated. For example, a response variable of interest can be measured repeatedly over time on the same subject and at the same time, an event time representing a terminating event is also obtained. Joint modelling using a shared random effect is useful for analyzing these data. Inferences based on marginal likelihood may involve the evaluation of analytically intractable integrations over the random-effect distributions. In this paper we propose a joint HGLM approach for analyzing such outcomes using the HGLM (hierarchical generalized linear model) method based on h-likelihood (i.e. hierarchical likelihood), which avoids these integration itself. The proposed method has been demonstrated using various numerical studies.
Survival Modelling with Various Random-Effect Structures
하일도 계명대학교 자연과학연구소 2014 Quantitative Bio-Science Vol.33 No.2
Simple frailty models such as shared frailty have been often used for analyzing clustered time-to-event data. However, under more complex structures such as nested design or multi-center trial, the extended models are required. In this paper we review multi-component semi-parametric survival models with various random-effect structures for analyzing the complex clustered time-to-event data. For the inferences we use hierarchical likelihood (h-likelihood), and also review how to select a proper model and also to select variables of fixed effects, which are very important in regression analysis. We discuss the relative advantages of h-likelihood method compared to marginal likelihood method. The h-likelihood method is illustrated using a well-known practical data set.
하일도,노맹석,이영조,임요한,이재용,오희석,신동완,이상구,서진욱,박용태,조성준,박종헌,김유경,유경상,Ha, Il Do,Noh, Maengseok,Lee, Youngjo,Lim, Johan,Lee, Jaeyong,Oh, Heeseok,Shin, Dongwan,Lee, Sanggoo,Seo, Jinuk,Park, Yonhtae,Cho, Sungzoon,Park 한국통계학회 2015 응용통계연구 Vol.28 No.2
본 논문에서는 SRC-Stat 통계패키지를 이용하여 생존자료를 분석하는 방법을 소개한다. 본 패키지는 단변량 생존 자료 분석을 위한 콕스의 비례위험모형 뿐만아니라, 다변량 생존자료분석을 위한 공통 및 지분 프레일티 모형과 같은 고급 생존분석법을 제공한다. 잘 알려져 있는 실제자료의 사용을 통해 본 패키지의 유용성을 예증한다. In this paper we introduce how to analyze survival data via a SRC-Stat statistical package. This provides classical survival analysis (e.g. Cox's proportional hazards models for univariate survival data) as well as advanced survival analysis such as shared and nested frailty models for multivariate survival data. We illustrate the use of our package with practical data sets.
A HGLM Approach for Clustered Insurance Claim Data
하일도,노맹석 한국자료분석학회 2009 Journal of the Korean Data Analysis Society Vol.11 No.6
Various insurance data with independent responses have been widely analyzed using generalized linear models(GLMs). Recently, the analyses of clustered insurance data which are usually clustered by policies, regions, divisions or individuals have been studied. Such data often have not only a correlation within clusters but also a heterogeneity between clusters. Thus the data can be analyzed by using extended GLMs with random effects. However, their usual inference methods require intractable integrations and don't provide a proper prediction for random effects. In this paper we propose a new framework for analyzing the clustered claim data using hierarchical GLMs(HGLMs. Lee and Nelder, 1996). For the inferences we use the hierarchical likelihood(h-likelihood) which avoids such difficult integrations and provides a statistically efficient procedure. Furthermore, we also propose how to construct the prediction intervals of random effects which are very useful in investigating a potential heterogeneity across clusters. The proposed method is illustrated with a real data set about the number of third party claims which are clustered by 13 divisions in Australia.
A Frailty Model for Crossing-Hazard Data
하일도 한국자료분석학회 2008 Journal of the Korean Data Analysis Society Vol.10 No.1
The crossing hazards are often observed in clinical trials on time-to-event. This indicates a strong non-proportional hazards (non-PH) structure and may occur due to a heterogeneity between survival data. Frailty, a random-effect term in hazard models, usually accounts such heterogeneity and the resulting marginal model, which is obtained by integrating out the frailty, leads to a non-PH model. Accordingly, in this paper we propose how to model the crossing hazards via a frailty and to fit the proposed model using SAS PROC NLMIXED. The proposed method is illustrated with a well-known data set from a randomized clinical trial on gastric cancer patients, where the aim of trial is to investigate the effect of chemotherapy (group 1) and combined chemotherapy and radiotherapy (group 2) on survival times of gastric cancer patients.
하일도,노규정,고정환,Ha, Il-Do,No, Gyu-Jeong,Go, Jeong-Hwan 한국데이터정보과학회 1996 한국데이터정보과학회지 Vol.7 No.1
본 논문은 의뢰인의 Pilot Study를 상담한 것으로서 당뇨병 및 암 환자에게 효능이 있는 약으로 밝혀진 Steroid계통의 Methyl Prednisolone이 척수손상 환자에게 효능이 있는지를 알아보기 위해, 토끼를 실험대상으로 하여 얻은 반복측정자료를 분석하였다.
Fitting Proportional Odds Survival Models with Random Effects via Binominal HGLMs
하일도 한국자료분석학회 2010 Journal of the Korean Data Analysis Society Vol.12 No.6
Recently, survival models with random effects have been widely used for the analysis of correlated survival data. In particular, frailty models(FMs) and proportional odds models (POMs) with random effects are useful. The FMs are based on the assumption of proportional hazards(PH), whereas POMs are not. The POM is a flexible non-PH model and give an interpretation of odds ratio in survival analysis. However, it is usually difficult to fit POMs with random effects because of integration about random effects and censoring. The hierarchical-likelihood for the random-effect POMs can be expressed as that for binominal hierarchical generalized linear models(HGLMs). Thus, in this paper we show how to fit easily the POMs via HGLMs. The proposed method is illustrated using a well-known correlated survival data set.
Analysis of Nested CGD Infection Data Using Multilevel Frailty Model
하일도,조건호 한국자료분석학회 2004 Journal of the Korean Data Analysis Society Vol.6 No.3
Multivariate event-time data with nested structures can be modelled by random-effect survival models such as multilevel frailty models. We show how to analyze the chronic granulomatous disease data(Fleming and Harrington, 1991), which comprise recurrent infection times of patients from different hospitals, using multilevel frailty model. Inferences are developed using hierarchical-likelihood, which provides a simple unified framework and a numerically efficient fitting algorithm for various random-effect models.
Laplace approximation approach for frailty survival models
하일도,조건호 한국데이터정보과학회 2018 한국데이터정보과학회지 Vol.29 No.6
For multivariate or correlated survival data semi-parametric frailty models with non-parametric baseline hazards, extensions of Cox's (1972) proportional hazards models, has been often used. The marginal likelihood has been usually used for the inferences, but it often requires the computation of difficult integration in integrating out the frailty terms. In this paper we propose a Laplace approximation approach based on hierarchical likelihood for the frailty models. The proposed method is demonstrated via simulation study and two well-known real data sets. In particular, the simulation results show that our method is better than standard h-likelihood method in terms of bias.