• Journal of Applied Reliability
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• v.16 no.1
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• pp.56-63
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• 2016

Purpose: The purpose of this study is to point out that the Kaplan-Meier method is not valid to calculate the survival probability or failure probability (risk) in the presence of competing risks and to introduce more valid method of cumulative incidence function. Methods: Survival analysis methods have been widely used in biostatistics division. However the same methods have not been utilized in reliability division. Especially competing risks cases, where several 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 noticed in the realm of reliability expertism or they are analysed in the wrong way. Specifically Kaplan-Meier method which assumes that the censoring times and failure times are independent 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 and sample competing risks data are analysed using cumulative incidence function and some graphs. Finally comparison of cumulative incidence functions and regression type analysis are mentioned briefly. Results: Cumulative incidence function is used to calculate the survival probability or failure probability (risk) in the presence of competing risks and some useful graphs depicting the failure trend over the lifetime are introduced. Conclusion: This paper shows that Kaplan-Meier method is not appropriate for the evaluation of survival or failure over the course of lifetime. In stead, cumulative incidence function is shown to be useful. Some graphs using the cumulative incidence functions are also shown to be informative.

• 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.

• Communications for Statistical Applications and Methods
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• v.23 no.6
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• pp.555-562
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• 2016

Interval censored data often occur in an observational study where the subject is followed periodically. Instead of observing an exact failure time, two inspection times that include it are available. There are several methods to analyze interval censored failure time data (Sun, 2006). However, in the presence of competing risks, few methods have been suggested to estimate covariate effect on interval censored competing risk data. A sub-distribution hazard model is a commonly used regression model because it has one-to-one correspondence with a cumulative incidence function. Alternatively, Klein and Andersen (2005) proposed a pseudo-value approach that directly uses the cumulative incidence function. In this paper, we consider an extension of the pseudo-value approach into the interval censored data to estimate regression coefficients. The pseudo-values generated from the estimated cumulative incidence function then become response variables in a generalized estimating equation. Simulation studies show that the suggested method performs well in several situations and an HIV-AIDS cohort study is analyzed as a real data example.

• 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).

• The Korean Journal of Applied Statistics
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• v.20 no.2
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• pp.357-371
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• 2007

For the time-to-failure data with competing risks, cumulative incidence functions (CIFs) are commonly estimated using nonparametric methods. If the cases of events due to the cause of primary interest are infrequent relative to other cause of failure, nonparametric methods may result in rather imprecise estimates for CIF. In such cases, Bryant et al. (2004) suggested to model the cause-specific hazard of primary interest parametrically, while accounting for the other modes of failure using nonparametric estimator. We represented the semiparametric cumulative incidence estimator and extended to the model of Weibull and log-normal distribution. We also conducted simulations to access the performance of the semiparametric cumulative incidence estimators and to investigate the impact of model misspecification in log-normal cause-specific hazard model.

• Communications for Statistical Applications and Methods
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• v.30 no.1
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• pp.1-19
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• 2023

Observational data with missing or incomplete data are common in biomedical research. Multiple imputation is an effective approach to handle missing data with the ability to decrease bias while increasing statistical power and efficiency. In recent years propensity score (PS) matching has been increasingly used in observational studies to estimate treatment effect as it can reduce confounding due to measured baseline covariates. In this paper, we describe in detail approaches to competing risk analysis in the setting of incomplete observational data when using PS matching. First, we used multiple imputation to impute several missing variables simultaneously, then conducted propensity-score matching to match statin-exposed patients with those unexposed. Afterwards, we assessed the effect of statin exposure on the risk of heart failure-related hospitalizations or emergency visits by estimating both relative and absolute effects. Collectively, we provided a general methodological framework to assess treatment effect in incomplete observational data. In addition, we presented a practical approach to produce overall cumulative incidence function (CIF) based on estimates from multiple imputed and PS-matched samples.

• Journal of the Korean Data and Information Science Society
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• v.26 no.6
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• pp.1271-1281
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• 2015

Competing risk analysis is widely applied to analyze a failure time with more than two causes. This paper discusses the application of a competing risk model to a economic activity state of workers with occupational injuries. In particular, main interest is to estimate the distribution of restarting time two kinds of economic activities, (i) returning to original working place and (ii) finding a new job. In this paper, we applied a cumulative incidence function to evaluate their patterns under several individual factors and working place's factor. Furthermore, a subdistributional regression model is applied to estimate the effect of these factors on the returning time. According to result, worker with higher education, younger age and longer working period had a higher chance to return an original working place while one with more severe injuries and skilled laborer had longer returning time to an original working place.

• Safety and Health at Work
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• v.12 no.2
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• pp.174-183
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• 2021

Background: Toluene diisocyanate (TDI) is a highly reactive chemical that causes sensitization and has also been associated with increased lung cancer. A risk assessment was conducted based on occupational epidemiologic estimates for several health outcomes. Methods: Exposure and outcome details were extracted from published studies and a NIOSH Health Hazard Evaluation for new onset asthma, pulmonary function measurements, symptom prevalence, and mortality from lung cancer and respiratory disease. Summary exposure-response estimates were calculated taking into account relative precision and possible survivor selection effects. Attributable incidence of sensitization was estimated as were annual proportional losses of pulmonary function. Excess lifetime risks and benchmark doses were calculated. Results: Respiratory outcomes exhibited strong survivor bias. Asthma/sensitization exposure response decreased with increasing facility-average TDI air concentration as did TDI-associated pulmonary impairment. In a mortality cohort where mean employment duration was less than 1 year, survivor bias pre-empted estimation of lung cancer and respiratory disease exposure response. Conclusion: Controlling for survivor bias and assuming a linear dose-response with facility-average TDI concentrations, excess lifetime risks exceeding one per thousand occurred at about 2 ppt TDI for sensitization and respiratory impairment. Under alternate assumptions regarding stationary and cumulative effects, one per thousand excess risks were estimated at TDI concentrations of 10 - 30 ppt. The unexplained reported excess mortality from lung cancer and other lung diseases, if attributable to TDI or associated emissions, could represent a lifetime risk comparable to that of sensitization.

• The Korean Journal of Pain
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• v.13 no.1
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• pp.31-37
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• 2000

Background: Recent studies suggested that a preoperative block of N-methyl-D-aspartate (NMDA) receptors with NMDA antagonists may reduce postoperative pain. In this double-blind study, magnesium sulfate, a natural NMDA receptor antagonist, was administered preoperatively to investigate the effects of magnesium sulfate on postoperative pain and pulmonary function. Methods: Seventy patients who were to undergo gastrectomy under general anesthesia were randomly assigned to one of three groups. Groups 2 and 3 received intravenous magnesium, preoperatively (Group 2: 50 mg/kg bolus, 7.5 mg/kg/hr for 20 hr, Group 3: 50 mg/kg bolus, 15 mg/kg/hr for 20 hr). Group 1 received normal saline as the control group. Visual analog scale (VAS) for postoperative pain and mood, cumulative analgesic consumption, recovery of pulmonary function and side effects were evaluated at 6, 24, 48 and 72 hours after the operation. Results: In Groups 2 and 3, plasma concentration of magnesium were significantly higher than in Group 1 at 6 and 20 hours after infusion (P<0.05). There were no significant differences in the analgesic consumption, and recovery of pulmonary function and the incidence of side effects at 6, 24, 48 and 72 hours after the operation among the three groups. In Group 3, pain scores at rest measured 24 and 48 hours after operation were lower than the control group, and pain scores when deep breathing were significantly lower than the control group at postoperative 6, 24, 48, and 72 hours. Conclusions: We conclude that intravenous infusion of greater amount of magnesium has little effectiveness in reducing postoperative pain. However, further studies are needed to characterize the clinical significance of these effects on postoperative pain.

• Journal of Dental Rehabilitation and Applied Science
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• v.27 no.4
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• pp.449-454
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• 2011

Recently, jawbone osteonecrosis has been largely reported as a potential adverse effect of bisphosphonate (BP)administration. Currently available published incidence data for BRONJ are based on retrospective studies and estimates of cumulative incidence range from 0.8 to 12%. The mandible is more commonly affected than the maxilla (2:1 ratio), and 60-70% of cases are preceded by a dental surgical procedure. The signs and symptoms that may occur before the appearance of clinical evident osteonecrosis include changes in the health of periodontal tissues, non-healing mucosal ulcers, loose teeth and unexplained soft-tissue infection. Tooth extraction as a precipitating event is a common observation. The significant benefits that bisphosphonates offer to patients clearly surpass the risk of potential side effects; however, any patient for whom prolonged bisphosphonate therapy is indicated, should be provided with preventive dental care in order to minimize the risk of developing this severe condition.