• Communications for Statistical Applications and Methods
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• v.16 no.2
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• pp.325-334
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• 2009

In this research, we propose a nonparametric test procedure for the right censored and grouped data under the additive hazards model. For deriving the test statistics, we use the likelihood principle. Then we illustrate proposed test with an example and compare the performance with other procedure by obtaining empirical powers. Finally we discuss some interesting features concerning the proposed test.

• Journal of the Korean Statistical Society
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• v.25 no.3
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• pp.433-444
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• 1996

In contrast to the multiplicative risk model, the additive risk model specifies that the hazard function with covariates is the sum of, rather than product of, the baseline hazard function and the regression function of covariates. We, in this paper, propose a method for checking the adequacy of the additive risk model based on partial-sum of matingale residuals. Under the assumed model, the asymptotic properties of the proposed test statistic and approximation method to find the critical values of the limiting distribution are studied. Several real examples are illustrated.

• Journal of the Korean Statistical Society
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• v.24 no.2
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• pp.537-549
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• 1995

In this article, we propose a class of weighted estimators for the excess risk in additive risk model with a binary covariate. The proposed estimator is consistent and asymptotically normal. When the assumed model is inappropriate, however, the estimators with different weights converge to nonidentical constants. This fact enables us to develop a goodness-of-fit test for the excess assumption by comparing estimators with diffrent weights. It is shown that the proposed test converges in distribution to normal with mean zero and is consistent under the model misspecifications. Furthermore, the finite-sample properties of the proposed test procedure are investigated and two examples using real data are presented.

• The Korean Journal of Applied Statistics
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• v.9 no.1
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• pp.75-89
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• 1996

This paper proposes a goodness-of-fit test for checking the adequacy of the additive risk model with a binary covariate. The test statistic is based on martingale residuals, which is the extended form of Wei(1984)'s test. The proposed test is shown to be consistent and asymptotically normally distributed under the regularity conditions. Furthermore, the test procedure is illustrated with two set of real data and the results are discussed.

• The Korean Journal of Applied Statistics
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• v.30 no.4
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• pp.539-553
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• 2017

We propose a multi-state model to analyze semi-competing risks data with interval-censored or missing intermediate events. This model is an extension of the three states of the illness-death model: healthy, disease, and dead. The 'diseased' state can be considered as the intermediate event. Two more states are added into the illness-death model to incorporate the missing events, which are caused by a loss of follow-up before the end of a study. One of them is a state of the lost-to-follow-up (LTF), and the other is an unobservable state that represents an intermediate event experienced after the occurrence of LTF. Given covariates, we employ the Lin and Ying additive hazards model with log-normal frailty and construct a conditional likelihood to estimate transition intensities between states in the multi-state model. A marginalization of the full likelihood is completed using adaptive importance sampling, and the optimal solution of the regression parameters is achieved through an iterative quasi-Newton algorithm. Simulation studies are performed to investigate the finite-sample performance of the proposed estimation method in terms of empirical coverage probability of true regression parameters. Our proposed method is also illustrated with a dataset adapted from Helmer et al. (2001).

• Communications for Statistical Applications and Methods
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• v.22 no.4
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• pp.313-320
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• 2015

In this research, we compare the efficiency of two test procedures proposed by Prentice and Gloeckler (1978) and Park and Hong (2009) for grouped data with possible right censored observations. Both test statistics were derived using the likelihood ratio principle, but under different semi-parametric models. We review the two statistics with asymptotic normality and consider obtaining empirical powers through a simulation study. The simulation study considers two types of models the location translation model and the scale model. We discuss some interesting features related to the grouped data and obtain null distribution functions with a re-sampling method. Finally we indicate topics for future research.

• Genomics & Informatics
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• v.14 no.4
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• pp.205-210
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• 2016

Interleukin-10 (IL10) plays an important role in initiating and maintaining an appropriate immune response to non-Hodgkin lymphoma (NHL). Previous studies have revealed that the transcription of IL10 mRNA and its protein expression may be infl uenced by several single-nucleotide polymorphisms in the promoter and intron regions, including rs1800896, rs1800871, and rs1800872. However, the impact of polymorphisms of the IL10 gene on NHL prognosis has not been fully elucidated. Here, we investigated the association between IL10 polymorphisms and NHL prognosis. This study involved 112 NHL patients treated at the National Cancer Center, Korea. The median age was 57 years, and 70 patients (62.5%) were men. Clinical characteristics, including age, performance status, stage, and extra-nodal involvement, as well as cell lineage and International Prognostic Index (IPI), were evaluated. A total of four polymorphisms in IL10 with heterozygous alleles were analyzed for hazard ratios of overall survival (OS) and progression-free survival (PFS) using Cox proportional hazards regression analysis. Diffuse large B-cell lymphoma was the most common histologic type (n = 83), followed by T-cell lymphoma (n = 18), mantle cell lymphoma (n = 6), and others (n = 5). Cell lineage, IPI, and extra-nodal involvement were predictors of prognosis. In the additive genetic model results for each IL10 polymorphism, the rs1800871 and rs1800872 polymorphisms represented a marginal association with OS (p = 0.09 and p = 0.06) and PFS (p = 0.05 and p = 0.08) in B-cell lymphoma patients treated with rituximab plus cyclophosphamide, doxorubicin, vincristine, and prednisone (R-CHOP). These findings suggest that IL10 polymorphisms might be prognostic indicators for patients with B-cell NHL treated with R-CHOP.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.17
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• pp.7097-7103
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• 2014

Over-expression of de novo lipogenesis (DNL) genes is associated with the prognosis of various types of cancers. However, the effects of single nucleotide polymorphisms (SNPs) in these genes on recurrence and survival of non-small cell lung cancer (NSCLC) patients after surgery are still unknown. In this study, a total of 500 NSCLC patients who underwent surgery treatment were included. Eight SNPs in 3 genes (ACACA, FASN and ACLY) of the DNL pathway were examined using the Sequenom iPLEX genotyping system. Multivariate Cox proportional hazards regression and Kaplan-Meier curves were used to analyze the association of SNPs with patient survival and tumour recurrence. We found that two SNPs in the FASN gene were significantly associated with the recurrence of NSCLC. SNP rs4246444 had a significant association with lung cancer recurrence under additive model (hazard ratio [HR], 0.82; 95% confidence interval [95%CI], 0.67-1.00; p=0.05). Under the dominant model, rs4485435 exhibited a significant association with recurrence (HR, 0.75; 95%CI, 0.56-1.01; p=0.05). Additionally, SNP rs9912300 in ACLY gene was significantly associated with overall survival in lung cancer patients (HR, 1.41; 95%CI, 1.02-1.94, p=0.04) under the dominant model. Further cumulative effect analysis showed moderate dose-dependent effects of unfavorable SNPs on both survival and recurrence. Our data suggest that the SNPs in DNL genes may serve as independent prognostic markers for NSCLC patients after surgery.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.62 no.4
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• pp.333-345
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• 2017

The potato tuber is known as a rich source of essential nutrients, used throughout the world. Although potato-breeding programs share some priorities, the major objective is to increase the genetic potential for yield through breeding or to eliminate hazards that reduce yield. Glycoalkaloids, which are considered a serious hazard to human health, accumulate naturally in potatoes during growth, harvesting, transportation, and storage. Here, we used the AMMI (additive main effects and multiplicative interaction) and GGE (Genotype main effect and genotype by environment interaction) biplot model, to evaluate tuber yield stability and glycoalkaloid content in six potato cultivars across three locations during 2012/2013. The environment on tuber yield had the greatest effect and accounted for 33.0% of the total sum squares; genotypes accounted for 3.8% and $G{\times}E$ interaction accounted for 11.1% which is the nest highest contribution. Conversely, the genotype on glycoalkaloid had the greatest effect and accounted for 82.4% of the total sum squares), whereas environment and $G{\times}E$ effects on this trait accounted for only 0.4% and 3.7%, respectively. Furthermore, potato genotype 'Superior', which covers most of the cultivated area, exhibited high yield performance with stability. 'Goun', which showed lower glycoalkaloid content, was the most suitable and desirable genotype. Results showed that, while tuber yield was more affected by the environment, glycoalkaloid content was more dependent on genotype. Further, the use of the AMMI and GGE biplot model generated more interactive visuals, facilitated the identification of superior genotypes, and suggested decisions on a variety of recommendations for specific environments.