• Journal of the Korean Data and Information Science Society
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• v.25 no.5
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• pp.1057-1067
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• 2014

Many genetic variants have been identified to be associated with complex diseases such as hypertension, diabetes and cancers throughout genome-wide association studies (GWAS). However, there still exist a serious missing heritability problem since the proportion explained by genetic variants from GWAS is very weak less than 10~15%. Gene-gene interaction study may be helpful to explain the missing heritability because most of complex disease mechanisms are involved with more than one single SNP, which include multiple SNPs or gene-gene interactions. This paper focuses on gene-gene interactions with the survival phenotype by extending the multifactor dimensionality reduction (MDR) method to the accelerated failure time (AFT) model. The standardized residual from AFT model is used as a residual score for classifying multiple geno-types into high and low risk groups and algorithm of MDR is implemented. We call this method AFT-MDR and compares the power of AFT-MDR with those of Surv-MDR and Cox-MDR in simulation studies. Also a real data for leukemia Korean patients is analyzed. It was found that the power of AFT-MDR is greater than that of Surv-MDR and is comparable with that of Cox-MDR, but is very sensitive to the censoring fraction.

• The Korean Journal of Applied Statistics
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• v.17 no.2
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• pp.253-267
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• 2004

In some clinical trials, one may see that a significant fraction of patients are cured and their original disease does not recur even after termination of treatment and pro-longed follow-up. This situation occurs frequently in pediatric cancer trials where there are excellent therapeutic results. In such cases, interest concentrated on the difference of cure rates rather than other types of differences in failure distributions. Various authors have investigated the parametric and nonparametric methods for testing the difference of cure rates. In this study, we compare by simulation the power and size of a parametric test and five nonparametric tests in a various range of the alternatives, censoring rates and cure rates. Our objectives are to determine if any test was preferable on the basis of size and power in various situation, and to investigate the effect of the model misspecification.

• The Korean Journal of Applied Statistics
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• v.33 no.6
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• pp.777-789
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• 2020

COVID-19 has been rapidly spread world wide since late December 2019. In this paper, our interest is to estimate distribution of incubation time defined as period between infection of virus and the onset. Due to the limit of accessibility and asymptomatic feature of COVID-19 virus, the exact infection and onset time are not always observable. For estimation of incubation time, interval censoring technique is implemented. Furthermore, a competing risk model is applied to estimate the case fatality and cure fraction. Based on the result, the mean incubation time is about 5.4 days and the fatality rate is higher for older and male patient and the cure rate is higher at younger,female and asymptomatic patient.