PURPOSE: In clinical studies, patients may experience several types of events during follow up under the competing risks (CR) framework. Patients are often classified into low- and high-risk groups based on prognostic factors. We propose a method to determine an optimal cutpoint value for prognostic factors on censored outcomes in the presence of CR. MATERIALS AND METHODS: We applied our method to data collected in a study of lung cancer patients. From September 1, 1991 to December 31, 2005, 758 lung cancer patients received tumor removal surgery at Samsung Medical Center in Korea. The proposed statistic converges in distribution to that of the supremum of a standardized Brownian bridge. To overcome the conservativeness of the test based on an approximation of the asymptotic distribution, we also propose a permutation test based on permuted samples. RESULTS: Most cases considered in our simulation studies showed that the permutation-based test satisfied a significance level of 0.05, while the approximation-based test was very conservative: the powers of the former were larger than those of the latter. The optimal cutpoint value for tumor size (unit: cm) prior to surgery for classifying patients into two groups (low and high risks for relapse) was found to be 1.8, with decent significance reflected as p values less than 0.001. CONCLUSION: The cutpoint estimator based on the maximally selected linear rank statistic was reasonable in terms of bias and standard deviation in the CR framework. The permutation-based test well satisfied type I error probability and provided higher power than the approximation-based test.
Bias (Epidemiology) ; Follow-Up Studies ; Humans ; Korea ; Lung Neoplasms ; Lung ; Methods

Radiation from natural sources is one of causes of the environmental diseases. Radon is the leading environmental cause of lung cancer next to smoking. To investigate the relationship between indoor radon concentrations and lung cancer, researchers must be able to estimate an individual’s cumulative level of indoor radon exposure and to do so, one must first be able to assess indoor radon concentrations. In this article, we outline factors affecting indoor radon concentrations and review related mathematical models based on the mass balance equation and the differential equations. Furthermore, we suggest the necessities of applying time-dependent functions for indoor radon concentrations and developing stochastic models.
Lung Neoplasms ; Models, Theoretical* ; Radon* ; Smoke ; Smoking

PURPOSE: To date most of finite element analysis assumed the presence of 100% contact between bone and implant, which is inconsistent with clinical reality. In human retrieval study bone-implant contact (BIC) ratio ranged from 20 to 80%. The objective of this study was to explore the influence of bone-implant contact pattern on bone of the interface using nonlinear 3-dimensional finite element analysis. MATERIALS AND METHODS: A computer tomography-based finite element models with two types of implant (Mark III Branemark(R), Inplant(R)) which placed in the maxillary 2nd premolar area were constructed. Two different degrees of bone-implant contact ratio (40, 70%) each implant design were simulated. 5 finite element models were constructed each bone-implant contact ratio and implant design, and sum of models was 40. The position of bone-implant contact was determined according to random shuffle method. Elements of bone-implant contact in group W (wholly randomized osseointegration) was randomly selected in terms of total implant length including cortical and cancellous bone, while ones in group S (segmentally randomized osseointegration) was randomly selected each 0.75 mm vertically and horizontally. RESULTS: Maximum von Mises strain between group W and group S was not significantly different regardless of bone-implant contact ratio and implant design (P=.939). Peak von Mises strain of 40% BIC was significantly lower than one of 70% BIC (P=.007). There was no significant difference between Mark III Branemark(R) and Inplant(R) in 40% BIC, while average of peak von Mises strain for Inplant(R) was significantly lower (4886 +/- 1034 microm/m) compared with MK III Branemark(R) (7134 +/- 1232 microm/m) in BIC 70% (P<.0001). CONCLUSION: Assuming bone-implant contact in finite element method, whether the contact elements in bone were wholly randomly or segmentally randomly selected using random shuffle method, both methods could be effective to be no significant difference regardless of sample size.
Bicuspid ; Finite Element Analysis ; Humans ; Nitrogen Mustard Compounds ; Sample Size ; Sprains and Strains

PURPOSE: The healthy worker effect is an important issue in occupational epidemiology. This study was conducted to propose a new method to test the relation between exposure and mortality in the presence of the healthy worker effect. METHODS: In this study, the healthy worker hire effect was assumed to operate as a confounding variable of health status at the beginning of employment and healthy worker survival effect as a confounding and intermediate variable of employment status. In addition, the proposed method reflects the length bias sampling caused by changing of an employment status. Simulation studies were also carried out to compare the proposed method with Cox's time dependent covariates models . RESULTS: The theoretical development of the healthy worker survival effect is based on the result that an observation with change of an employment status requires that the survival time without intermediate event exceeds the waiting time for the intermediate event. According to our simulation studies, both the proposed method and Cox's time dependent covariates model which includes the change of employment status as time dependent covariates seem to be satisfactory at 5% significance level. However, Cox's time dependent covariates models without or with the change of employment status as time fixed covariate are unsatisfactory. The proposed test is superior in power to tests based on Cox's model. CONCLUSIONS: The healthy worker effect may not be controlled by classical Cox's proportional hazards models. The proposed method performed well in the presence of healthy worker effect in terms of level and power
Bias (Epidemiology) ; Confounding Factors (Epidemiology) ; Employment ; Epidemiology ; Healthy Worker Effect* ; Mortality ; Proportional Hazards Models

Purpose: The illness-death model (IDM) is a comprehensive approach to evaluate the relationship between relapse and death. This study aimed to illustrate the value of the IDM for identifying risk factors and evaluating predictive probabilities for relapse and death in patients with non–small cell lung cancer (NSCLC) in comparison with the disease-free survival (DFS) model. Materials and Methods: We retrospectively analyzed 612 NSCLC patients who underwent a curative operation. Using the IDM, the risk factors and predictive probabilities for relapse, death without relapse, and death after relapse were simultaneously evaluated and compared to those obtained from a DFS model. Results: The IDM provided more detailed risk factors according to the patient’s disease course, including relapse, death without relapse, and death after relapse, in patients with resected lung cancer. In the IDM, history of malignancy (other than lung cancer) was related to relapse and smoking history was associated with death without relapse; both were indistinguishable in the DFS model. In addition, the IDM was able to evaluate the predictive probability and risk factors for death after relapse; this information could not be obtained from the DFS model. Conclusion Compared to the DFS model, we found that the IDM provides more comprehensive information on transitions between states and disease stages and provides deeper insights with respect to understanding the disease process among lung cancer patients.

PURPOSE: This study was to provide the methods of developing the growth charts and the blood pressure nomogram among Korean children and adolescents. METHODS: The growth charts were developed based on the data from the national growth surveys for children and adolescents in 1998 and 2005. The percentile charts were developed through two stages. At the first stage, the selected empirical charts were smoothed through several fitting procedures including parametric and non-parametric methods. At the second stage, a modified LMS (lambda, mu, sigma) statistical procedure was applied to the smoothed percentile charts. The LMS procedure allowed to estimate any percentile and to calculate standard deviation units and z-scores. The charts for weight-for-age, height-for-age, BMI-for-age, weight-for-height and head circumference-for-age were developed by sex. Age and normalized height controlled sex-specific nomograms of systolic and diastolic blood pressure were developed by a fixed effect model of general regression using the data from 2005 national growth survey. RESULTS: The significant systemic differences between the percentiles of growth charts and the empirical data were not found. The final output of the study is available from Korean Center for Disease Control and Prevention homepage, http://www.cdc.go.kr/webcdc/. Blood Pressure nomogram was tabulated by height percentiles and age using the regression coefficients analyzed with regression model. CONCLUSION: 2007 growth charts and blood pressure nomogram were the first products based on the statistical modeling using the national survey data. The further study on the methodology including data collection, data cleaning and statistical modeling for representative growth charts would be needed.
Adolescent ; Blood Pressure ; Centers for Disease Control and Prevention (U.S.) ; Child ; Data Collection ; Growth Charts ; Head ; Humans ; Models, Statistical ; Nomograms