A visualizing method for investigating individual frailties using frailtyHL R-package

Ha, Il Do; Noh, Maengseok

doi:10.7465/jkdi.2013.24.4.931

Cited by 6 publications

(4 citation statements)

References 19 publications

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“…An advantage of our joint HGLM method is ease of extensibility to models with randomeffect terms, in which the integration to obtain the marginal likelihood is often intractable Cho, 2012, 2015;Ha and Noh, 2013). The separate modelling does not consider the dependency between two types of responses, whereas the joint modelling does.…”

Section: Discussionmentioning

confidence: 99%

Joint HGLM approach for repeated measures and survival data

Ha¹

2016

Journal of the Korean Data and Information Science Society

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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.

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Section: Discussionmentioning

confidence: 99%

Joint HGLM approach for repeated measures and survival data

Ha¹

2016

Journal of the Korean Data and Information Science Society

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“…In particular, it gives statistical justifications for inferences of random effects and also useful materials for data analysis (Ha and Noh, 2013;Paik et al, 2015).…”

Section: Poisson Hglms and H-likelihoodmentioning

confidence: 99%

Variable selection in Poisson HGLMs using h-likelihoood

Ha¹,

Cho²

2015

Journal of the Korean Data and Information Science Society

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Selecting relevant variables for a statistical model is very important in regression analysis. Recently, variable selection methods using a penalized likelihood have been widely studied in various regression models. The main advantage of these methods is that they select important variables and estimate the regression coefficients of the covariates, simultaneously. In this paper, we propose a simple procedure based on a penalized h-likelihood (HL) for variable selection in Poisson hierarchical generalized linear models (HGLMs) for correlated count data. For this we consider three penalty functions (LASSO, SCAD and HL), and derive the corresponding variable-selection procedures. The proposed method is illustrated using a practical example.

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“…These improvements include making statistical summaries more visual and providing more information at the same time (Symanzik and Carr, 2008), and they were further practiced in many researches (Cho, 2012;Han et al, 2012;Wong et al, 2012;Lee et al, 2013;Ha and Noh, 2013;Cho, 2014).…”

Section: Related Workmentioning

confidence: 99%

Visualization and interpretation of cancer data using linked micromap plots

Park¹,

Ahn²

2014

Journal of the Korean Data and Information Science Society

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The causes of cancer are diverse, complex, and only partially understood. Many factors including health behaviors, socioeconomic environments and geographical locations can directly damage genes or combine with existing genetic faults within cells to cause cancerous mutations. Collecting the cancer data and reporting the statistics, therefore, are important to help identify health trends and establish normal health changes in geographical areas. In this article, we analyzed cancer data and demonstrated how spatial patterns of the age-standardized rate and health indicators can be examined visually and simultaneously using linked micromap plots. As a result of data analysis, the age-standardized rate has positive correlativity with thyroid and breast cancer, but the rate has negative correlativity with smoking and drinking. In addition, the regions with high age-standardized rate are located in southwest and the areas of high population density while the standardized mortality ratio is higher in southwest and northeast where there are lots of rural areas.

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A visualizing method for investigating individual frailties using frailtyHL R-package

Cited by 6 publications

References 19 publications

Joint HGLM approach for repeated measures and survival data

Joint HGLM approach for repeated measures and survival data

Variable selection in Poisson HGLMs using h-likelihoood

Visualization and interpretation of cancer data using linked micromap plots

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