• Proceedings of the Korean Reliability Society Conference
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• 2000.04a
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• pp.185-191
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• 2000

A survival variable is a nonnegative random variable X with distribution function F and a survival function (equation omitted)=1－F. This variable is said to be New Better than Used of specified age $t_{0}$ if (equation omitted) for all $\chi$$\geq$0 and a fixed to. We propose the test for $H_{0}$ : (equation omitted) for all $\chi$$\geq$0 against $H_1$：(equation omitted) for all $\chi$$\geq$0 when the specified age $t_{0}$ is unknown but can be estimated from the data when $t_{0}$=${\mu}$, the mean of F, and also when $t_{0}$=$\xi_p$, the pth percentile of F. This test statistic, which is based on a linear function of the order statistics from the sample, is readily applied in the case of small sample. Also, this test statistic is more simple than the test statistic of Ahmad's test statistic (1998). Finally, the performance of this test is presented.

• Communications for Statistical Applications and Methods
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• v.31 no.1
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• pp.155-178
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• 2024

Regression discontinuity (RD) design is one of the most widely used methods in causal inference for estimation of treatment effect when the treatment is created by a cutpoint from the covariate of interest. There has been little attention to RD design, although it provides a very useful tool for analysis of treatment effect for censored data. In this paper, we define the causal effect for survival function in RD design when the treatment is assigned deterministically by the covariate of interest. We propose estimators of this causal effect for survival data by using transformation, which leads unbiased estimator of the survival function with local linear regression. Simulation studies show the validity of our approach. We also illustrate our proposed method using the prostate, lung, colorectal and ovarian (PLCO) dataset.

• Journal of Veterinary Clinics
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• v.18 no.4
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• pp.358-362
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• 2001

Brain dead (BD) patients remain the largest source of solid organs for transplantation. BD has shown to decrease graft function and survival in rodent models. The aim of this study was to evaluate how brain death affects graft viability in the donor and kidney tolerance to cold preservation as assessed by survival in a canine transplantation. 13 Beagle dogs were used for the study. Brain death was induced by the sudden inflation of a subdural balloon catheter with continuous monitoring of arterial blood pressure and eletroencephalographic activity (n=3). Sixteen hours after conformation of brain death, kidney graft were retrieved (n=6). Non-BD donors served as controls (n=4). All kidneys were flushed with University of Wisconsin (UW) solution and preserved for 24 hours at 4$^{\circ}C$ before transplantation. Recipient survival rates, serum creatinine level were analyzed. Brain death induced the well-known Cushing reaction with a severe increase in blood pressure and tachycardia. Thereafter, cardiac function returned progressively to baseline within 8 hours and remained stable until the end of the experiment. All of dogs in both group transplanted were survived until 7 days (100%), and the kidneys showed functional early rejection at 8.3$\pm$0.5 days and 8.5$\pm$0.5 days after transplantation, in BD and allograft group, respectively. BD kidneys were functionally similar to control kidneys for 7 days after transplantated. Brain death has no deleterious effect on preservation injury and survival of dog kidney transplantation, although it induces changes in hemodynamic parameters. This study reveals that kidneys from BD donors do not exhibit more ischemia reperfusion injury, and support good early function and survival.

• International Journal of Reliability and Applications
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• v.2 no.1
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• pp.49-56
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• 2001

In this paper we consider empirical Bayes estimation of the hazard rate and survival probabilities with right censored data under the assumption that the hazard function is constant over the period of observation and the prior distribution is gamma. We provide an estimator of the first derivative of the prior moment generating function that converges at each point to the true value in $L_2$ and use it to obtain, easy to compute, asymptotically optimal estimators under the squared error loss function.

• Journal of the Korean Data and Information Science Society
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• v.11 no.2
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• pp.225-233
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• 2000

In life-testing experiments, in which the longest time an experimental unit is on test is not a failure time, but rather a censored observation. For the situation the Kaplan-Meier estimator is known to be a baised estimator of the survival function. Several modifications of the Kaplan-Meier estimator are examined and compared with bias and mean squared error.

• Journal of the Korean Data and Information Science Society
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• v.9 no.1
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• pp.37-45
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• 1998

We consider the problem of obtaining the confidence bands for the survival function with incomplete data. It is a rather simple procedure for constructing confidence bands of survival function. This method uses the weak convergence of normalized cumulative hazard estimator to a mean zero Gaussian process whose distribution can be easily approximated through simulation. Finally, we compare the performance of the proposed confidence bands through Monte Carlo simulation and we applied to construct the proposed bands with the Leukemia patient data.

• The Korean Journal of Applied Statistics
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• v.24 no.5
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• pp.871-881
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• 2011

In this paper we propose a logistic regression method to estimate the survival function and the median survival time in interval-censored data. The proposed method is motivated by the data augmentation technique with no sacrifice in augmenting data. In addition, we develop a cross validation criterion to determine the size of data augmentation. We compare the proposed estimator with other existing methods such as the parametric method, the single point imputation method, and the nonparametric maximum likelihood estimator through extensive numerical studies to show that the proposed estimator performs better than others in the sense of the mean squared error. An illustrative example based on a real data set is given.

• The Korean Journal of Applied Statistics
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• v.22 no.6
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• pp.1345-1353
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• 2009

We introduce a simple methodology, so-called generalized gamma-polynomial approximation, based on moment-matching technique to approximate survival and hazard functions in the context of parametric survival analysis. We use the generalized gamma-polynomial approximation to approximate the density and distribution functions of convolutions and finite mixtures of random variables, from which the approximated survival and hazard functions are obtained. This technique provides very accurate approximation to the target functions, in addition to their being computationally efficient and easy to implement. In addition, the generalized gamma-polynomial approximations are very stable in middle range of the target distributions, whereas saddlepoint approximations are often unstable in a neighborhood of the mean.

• Journal of Periodontal and Implant Science
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• v.53 no.3
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• pp.194-206
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• 2023

Purpose: A novel attachment system for implant-retained overdentures (IRODs) with novel material combinations for improved mechanical resilience and prosthodontic success (Novaloc) has been recently introduced as an alternative to an existing system (Locator). This study investigated whether differences between the Novaloc and Locator attachment systems translate into differences in implant survival, implant success, and patient-centered outcomes when applied in a real-world in-practice comparative setting in patients restored with mandibular IRODs supported by 2 interforaminal implants (2-IRODs). Methods: This prospective, intra-subject crossover comparison compared 20 patients who received 2 intra-foraminal bone level tapered implants restored with full acrylic overdentures using either the Locator or Novaloc attachment system. After 6 months of function, the attachment in the corresponding dentures was switched, and the definitive attachment system type was delivered based on the patient's preference after 12 months. For the definitive attachment system, implant survival was evaluated after 24 months. The primary outcomes of this study were oral health-related quality of life and patient preferences related to prosthetic and implant survival. Secondary outcomes included implant survival rate and success, prosthetic survival, perceived general health, and patient satisfaction. Results: Patient-centered outcomes and patient preferences between attachment systems were comparable, with relatively high overall patient satisfaction levels for both attachment systems. No difference in the prosthetic survival rate between study groups was detected. The implant survival rate over the follow-up period after 24 months in both groups was 100%. Conclusions: The results of this in-practice comparison indicate that both attachment systems represent comparable candidates for the prosthodontic retention of 2-IRODs. Both systems showed high rates of patient satisfaction and implant survival. The influence of material combinations of the retentive system on treatment outcomes between the tested systems remains inconclusive and requires further investigations.

• Journal of Applied Reliability
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• v.18 no.2
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• pp.130-142
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• 2018

Purpose: The purpose of this study is to introduce regression method in the presence of competing risks and to show how you can use the method with hypothetical data. Methods: Survival analysis has been widely used in biostatistics division. But the same method has not been utilized in reliability division. Especially competing risks, where more than a couple of causes of failure occur and the occurrence of one event precludes the occurrence of the other events, are scattered in reliability field. But they are not utilized in the area of reliability or they are analysed in the wrong way. Specifically Kaplan-Meier method is used to calculate the probability of failure in the presence of competing risks, thereby overestimating the real probability of failure. Hence, cumulative incidence function is introduced. In addition, sample competing risks data are analysed using cumulative incidence function along with some graphs. Lastly we compare cumulative incidence functions with regression type analysis briefly. Results: We used cumulative incidence function to calculate the survival probability or failure probability in the presence of competing risks. We also drew some useful graphs depicting the failure trend over the lifetime. Conclusion: This research shows that Kaplan-Meier method is not appropriate for the evaluation of survival or failure over the course of lifetime in the presence of competing risks. Cumulative incidence function is shown to be useful in stead. Some graphs using the cumulative incidence functions are also shown to be informative.