Sasang typology is a traditional Korean medical classification scheme that combines management with general medicine and can be applied to chronic diseases. We aimed analyze differences in Personality Questionnaire (SPQ) Digestive Function Inventory (SDFI) results patients diabetes mellitus (DM), hypertension, functional dyspepsia, major depressive disorder (MDD), adenomyosis. In this cross‐sectional study, data were collected at college hospital South Korea. A total of 248 included: 52 DM,...

We introduce a multivariate functional principal component analysis (MFPCA) residual control chart for data. Our method utilizes the vine copula technique and is applied to high-frequency financial employ eigenfunctions uncover hidden dependence structures explain variations in sparse longitudinal data through MFPCA. With these eigenfunctions, we create copula-based To handle this context, predictive mean matching imputation. As part of real-world applications, conduct on time series five...

Despite the use of standardized protocols in, multi-centre, randomized clinical trials, outcome may vary between centres. Such heterogeneity alter interpretation and reporting treatment effect. Below, we propose a general frailty modelling approach for investigating, inter alia, putative treatment-by-centre interactions in time-to-event data multi-centre trials. A correlated random effects model is used to baseline risk effect across It be based on shared, individual or effects. For...

AbstractFrailty models extend proportional hazards to multivariate survival data. Hierarchical-likelihood provides a simple unified framework for various random effect such as hierarchical generalized linear models, frailty and mixed with censoring. Wereview the hierarchical-likelihood estimation methods models. can be expressed that Poisson Frailty thus fitted using Properties of new methodology are demonstrated by simulation. The method reduces bias maximum likelihood penalized...

For the analysis of competing risks data, three different types hazard functions have been considered in literature, namely cause-specific hazard, sub-distribution and marginal function. Accordingly, medical researchers can fit Cox model to estimate effect covariates on each While relationship between has extensively studied, function not yet analyzed due difficulties related non-identifiability. In this paper, we adopt an assumed copula deal with identifiability issue, making it possible...

With the development of high-throughput technologies, more and high-dimensional or ultra-high-dimensional genomic data are being generated. Therefore, effectively analyzing such has become a significant challenge. Machine learning (ML) algorithms have been widely applied for modeling nonlinear complicated interactions in variety practical fields as survival data. Recently, multilayer deep neural network (DNN) models made remarkable achievements. Thus, Cox-based DNN prediction model (DNNSurv...

The proportional subdistribution hazards model (i.e. Fine‐Gray model) has been widely used for analyzing univariate competing risks data. Recently, this extended to clustered data via frailty. To the best of our knowledge, however, there no literature on variable selection method such frailty models. In paper, we propose a simple but unified procedure penalized h‐likelihood (HL) fixed effects in general class hazard models, which random may be shared or correlated. We consider three penalty...

Statistical process control for count data has difficulty overcoming multicollinearity. In this paper, we propose a new deep learning residual chart based on the asymmetrical response variable when there are highly correlated explanatory variables. We implement and compare different methods such as neural network, learning, principal component analysis Poisson regression, negative binomial nonlinear regression in terms of root mean squared error. Using two simulated datasets generated by...

Various frailty models have been developed and are now widely used for analysing multivariate survival data. It is therefore important to develop an information criterion model selection. However, in there several alternative ways of forming a the particular chosen may not be uniformly best. In this paper, we study Akaike (AIC) on selecting structure from set (possibly) non-nested models. We propose two new AIC criteria, based conditional likelihood extended restricted (ERL) given by Lee...

The frailty model, an extension of the proportional hazards is often used to model clustered survival data. However, some ordinary required when there exist competing risks within a cluster. Under risks, underlying processes affecting events interest and could be different but correlated. In this paper, hierarchical likelihood method proposed infer cause‐specific hazard for incorporates fixed effects as well random into extended function, so that does not require intensive numerical methods...

Competing risks data often exist within a center in multi-center randomized clinical trials where the treatment effects or baseline may vary among centers. In this paper, we propose subdistribution hazard regression model with multivariate frailty to investigate heterogeneity centers from trials. For inference, develop hierarchical likelihood (or h-likelihood) method, which obviates need for an intractable integration over terms. We show that profile function derived h-likelihood is...

Journal Article Comparison of hierarchical likelihood versus orthodox best linear unbiased predictor approaches for frailty models Get access Il Do Ha, Ha Search other works by this author on: Oxford Academic Google Scholar Youngjo Lee Biometrika, Volume 92, Issue 3, September 2005, Pages 717–723, https://doi.org/10.1093/biomet/92.3.717 Published: 01 2005

AbstractVariable selection methods using a penalized likelihood have been widely studied in various statistical models. However, semiparametric frailty models, these relatively less because the marginal function involves analytically intractable integrals, particularly when modeling multicomponent or correlated frailties. In this article, we propose simple but unified procedure via h-likelihood (HL) for variable of fixed effects general class which random may be shared, nested, correlated....

Abstract. Frailty models with a non-parametric baseline hazard are widely used for the analysis of survival data. However, their maximum likelihood estimators can be substantially biased in finite samples, because number nuisance parameters associated increases sample size. The penalized partial based on first-order Laplace approximation still has non-negligible bias. second-order to modified marginal bias reduction is infeasible presence too many complicated terms. In this article, we find...

Accelerated failure time (AFT) models allowing for random effects are linear mixed under the log‐transformation of survival with censoring and describe dependence in correlated data. It is well known that AFT useful alternatives to frailty models. To best our knowledge, however, there no literature on variable selection methods such In this paper, we propose a simple but unified variable‐selection procedure fixed random‐effect using penalized h‐likelihood (HL). We consider four penalty...

Competing risks data often occur within a center in multi-center clinical trials where the event times may be correlated due to unobserved factors across individuals. In this paper, we consider cause-specific proportional hazards model with shared frailty association between framework of competing risks. We use hierarchical likelihood approach, which does not require any intractable integration over terms. trial, cause death information observed for some patients. such case, analyses through...

With the increasing popularity of big data analysis, research on zero-inflated count with copula method has garnered significant attention because do not follow a normal distribution and have high correlation among variables. Within domain quality control, there been limited emphasis multivariate statistical process control (SPC) techniques that specifically address challenge multicollinearity within regression models for responses. In this paper, we explain computational in handling...

Semi-parametric frailty models are widely used to analyze clustered survival data. In this article, we propose the use of hierarchical likelihood interval for individual frailties. We study relationship between likelihood, empirical Bayesian, and fully Bayesian intervals show that our proposed can be interpreted as a frequentist confidence credible under uniform prior. also an adjustment avoid null intervals. Simulation studies preserves nominal level. The procedure is illustrated using data...