• The Korean Journal of Applied Statistics
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• v.29 no.7
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• pp.1231-1246
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• 2016

Cause-specific hazard model (Prentice et al., 1978) and subdistribution hazard model (Fine and Gray, 1999) are mostly used for the right censored survival data with competing risks. Some other models for survival data with competing risks have been subsequently introduced; however, those models have not been popularly used because the models cannot provide reliable statistical estimation methods or those are overly difficult to compute. We introduce simple and reliable competing risk regression models which have been recently proposed as well as compare their methodologies. We show how to use SAS and R for the data with competing risks. In addition, we analyze survival data with two competing risks using five different models.

• The Korean Journal of Applied Statistics
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• v.31 no.4
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• pp.529-538
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• 2018

Tuberculosis causes high morbidity and mortality. However, Korea still has the highest tuberculosis (TB) incidence and mortality among OECD countries despite decreasing incidence and mortality due to the development of modern medicine. Korea has now implemented various policy projects to prevent and control tuberculosis. This study analyzes the effects of public-private mix (PPM) tuberculosis control program on treatment outcomes and identifies the factors that affecting the success of TB treatment. We analyzed 130,000 new tuberculosis patient cohort from 2012 to 2015 using data of tuberculosis patient reports managed by the Disease Control Headquarters. A cumulative incidence function (CIF) compared the cumulative treatment success rates for each factor. We compared the results of the analysis using two popular types of competition risk models (cause-specific Cox's proportional hazards model and subdistribution hazard model) that account for the main event of interest (treatment success) and competing events (death).

• Bulletin of the Korean Mathematical Society
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• v.47 no.3
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• pp.551-561
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• 2010

In this paper we give a new characterization of real hypersurfaces of type B, that is, a tube over a totally geodesic $\mathbb{Q}P^n$ in complex two-plane Grassmannians $G_2(\mathbb{C}^{m+2})$, where m = 2n, with the Reeb vector $\xi$ belonging to the distribution $\mathfrak{D}$, where $\mathfrak{D}$ denotes a subdistribution in the tangent space $T_xM$ such that $T_xM$ = $\mathfrak{D}{\bigoplus}\mathfrak{D}^{\bot}$ for any point $x{\in}M$ and $\mathfrak{D}^{\bot}=Span{\xi_1,\;\xi_2,\;\xi_3}$.

• The Korean Journal of Applied Statistics
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• v.30 no.2
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• pp.233-241
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• 2017

Interval censored data frequently occur in observation studies where the subject is followed periodically. In this paper, our interest is to suggest a test statistic to compare the CIF of two groups with interval censored failure time data in the presence of competing risks. Gray (1988) suggested a test statistic for right censored data that motivated a well-known Fine and Gray's subdistribution hazard model. A multiple imputation technique is adopted to adopt Gray's test statistic to interval censored data. The powers and sizes of the suggested method are investigated through diverse simulation schemes. The main merit of the suggested method is its simplicity to implement with existing software for right censored data. The method is illustrated by analyzing Bangkok's HIV cohort dataset.

• Journal of Chest Surgery
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• v.55 no.3
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• pp.189-196
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• 2022

Background: This study investigated the predictive value of the frailty index calculated using laboratory data and vital signs (FI-L) in patients who underwent coronary artery bypass grafting (CABG). Methods: This study included 508 patients (age 67.3±9.7 years, male 78.0%) who underwent CABG between 2018 and 2021. The FI-L, which estimates patients' frailty based on laboratory data and vital signs, was calculated as the ratio of variables outside the normal range for 32 preoperative parameters. The primary endpoints were operative and medium-term all-cause mortality. The secondary endpoints were early postoperative complications and major adverse cardiac and cerebrovascular events (MACCEs). Results: The mean FI-L was 20.9%±10.9%. The early mortality rate was 1.6% (n=8). Postoperative complications were atrial fibrillation (n=148, 29.1%), respiratory complications (n=38, 7.5%), and acute kidney injury (n=15, 3.0%). The 1- and 3-year survival rates were 96.0% and 88.7%, and the 1- and 3-year cumulative incidence rates of MACCEs were 4.87% and 8.98%. In multivariable analyses, the FI-L showed statistically significant associations with medium-term all-cause mortality (hazard ratio [HR], 1.042; 95% confidence interval [CI], 1.010-1.076), MACCEs (subdistribution HR, 1.054; 95% CI, 1.030-1.078), atrial fibrillation (odds ratio [OR], 1.02; 95% CI, 1.002-1.039), acute kidney injury (OR, 1.06; 95% CI, 1.014-1.108), and re-operation for bleeding (OR, 1.09; 95% CI, 1.032-1.152). The minimal p-value approach showed that 32% was the best cutoff for the FI-L as a predictor of all-cause mortality post-CABG. Conclusion: The FI-L was a significant prognostic factor related to all-cause mortality and postoperative complications in patients who underwent CABG.