• Proceedings of the Korean Statistical Society Conference
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• 2002.11a
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• pp.197-201
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• 2002

The healthy worker effect is an important issue in occupational epidemiology. We proposed a new statistical method to test the relationship between exposure and time to death in the presence of the healthy worker effect. In this study, we considered the healthy worker hire effect to operate as a confounder and the healthy worker survival effect to operate as a confounder and an intermediate variable. The basic idea of the proposed method reflects the length bias-sampling caused by changing one's employment status. Simulation studies were also carried out to compare the proposed method with the Cox proportional hazards models. According to our simulation studies, both the proposed test and the test based on the Cox model having the change of the employment status as a time-dependent covariate seem to be satisfactory at an upper 5% significance level. The Cox models, however, are inadequate with the change, if any, of the employment status as time-independent covariate. The proposed test is superior in power to the test based on the Cox model including the time-dependent employment status.

• Journal of the Korean Data and Information Science Society
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• v.24 no.4
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• pp.877-883
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• 2013

The quadratic inference functions (QIF) method proposed by Qu et al. (2000) and the generalized method of moments (GMM) for marginal regression analysis of longitudinal data with time-dependent covariates proposed by Lai and Small (2007) both are the methods based on generalized method of moment (GMM) introduced by Hansen (1982) and both use generalized estimating equations (GEE). Lai and Small (2007) divided time-dependent covariates into three types such as: Type I, Type II and Type III. In this paper, we compared these methods in the case of Type II and Type III in which full covariates conditional mean assumption (FCCM) is violated and interested in whether they can improve the results of GEE with independence working correlation. We show that in the marginal regression model with Type II time-dependent covariates, GMM Type II of Lai and Small (2007) provides more ecient result than QIF and for the Type III time-dependent covariates, QIF with independence working correlation and GMM Type III methods provide the same results. Our simulation study showed the same results.

• Communications for Statistical Applications and Methods
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• v.8 no.1
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• pp.127-135
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• 2001

In this paper, we consider a simple method for testing the assumption of independent censoring on the basis of a Cox proportional hazards regression model with a time-dependent covariate. This method involves a two-stage sampling in which a random subset of censored observations is selected and followed-up until their true survival times are observed. Lee and Wolfe(1998) proposed an adjusted estimate of the survivor function for the dependent censoring under a proportional hazards alternative. This paper extends their result to obtain a bootstrap confidence interval for the adjusted survivor function under the dependent censoring. The proposed procedure is illustrated with an example of a clinical trial for lung cancer analysed in Lee and Wolfe(1998).

• Journal of Korea Water Resources Association
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• v.42 no.6
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• pp.445-455
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• 2009

In this paper proportional hazards models for the first through seventh break of 150 mm cast iron pipes in a case study area are established. During the modeling process the assumption of the proportional hazards for covariates on the hazards is examined to include the time-dependent covariate terms in the models. As a result, the pipe material/joint type and the number of customers are modeled as time-dependent for the first failure, and for the second failure only the number of customers is modeled as time-dependent. From the analysis on the baseline hazard functions the failure hazards are found to be generally increasing for the first and second failure, while the hazards of the third break and beyond showed a form of a bath-tub. Furthermore, the changes in the baseline hazard rates according to the time and number of break reflect that the general condition of the pipes is deteriorating. The factors causing pipe break and their effects are analyzed based on the estimated regression coefficients and their hazard ratios, and the constructed models are verified using the deviance residuals of the models.

• Journal of Gastric Cancer
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• v.15 no.4
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• pp.238-245
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• 2015

Purpose: This study aimed to identify time-dependent prognostic factors and demonstrate the time-dependent effects of important prognostic factors in patients with advanced gastric cancer (AGC). Materials and Methods: We retrospectively evaluated 3,653 patients with AGC who underwent curative standard gastrectomy between 1991 and 2005 at the Korea Cancer Center Hospital. Multivariate survival analysis with Cox proportional hazards regression was used in the analysis. A non-proportionality test based on the Schoenfeld residuals (also known as partial residuals) was performed, and scaled Schoenfeld residuals were plotted over time for each covariate. Results: The multivariate analysis revealed that sex, depth of invasion, metastatic lymph node (LN) ratio, tumor size, and chemotherapy were time-dependent covariates violating the proportional hazards assumption. The prognostic effects (i.e., log of hazard ratio [LHR]) of the time-dependent covariates changed over time during follow-up, and the effects generally diminished with low slope (e.g., depth of invasion and tumor size), with gentle slope (e.g., metastatic LN ratio), or with steep slope (e.g., chemotherapy). Meanwhile, the LHR functions of some covariates (e.g., sex) crossed the zero reference line from positive (i.e., bad prognosis) to negative (i.e., good prognosis). Conclusions: The time-dependent effects of the prognostic factors of AGC are clearly demonstrated in this study. We can suggest that time-dependent effects are not an uncommon phenomenon among prognostic factors of AGC.

• The Korean Journal of Applied Statistics
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• v.36 no.5
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• pp.415-445
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• 2023

Oftentimes, the time dependent treatment covariate and the time dependent confounders exist in observation studies. It is an important problem to correctly adjust for the time dependent confounders in the propensity score analysis. Recently, In the survival data, Hade et al. (2020) used a propensity score matching method to correctly estimate the treatment delay effect when the time dependent confounder affects time to the treatment time, where the treatment delay effects is defined to the delay in treatment reception. In this paper, we proposed the Cox model based marginal structural model (Cox-MSM) framework to estimate the treatment delay effect and conducted extensive simulation studies to compare our proposed Cox-MSM with the propensity score matching method proposed by Hade et al. (2020). Our simulation results showed that the Cox-MSM leads to more exact estimate for the treatment delay effect compared with two sequential matching schemes based on propensity scores. Example from study in treatment discontinuation in conjunction with simulated data illustrates the practical advantages of the proposed Cox-MSM.

• Journal of Korean Society of Transportation
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• v.29 no.4
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• pp.29-41
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• 2011

This study investigates the factor that influences commuting time to work when individuals allocate their time for different types of activities. The commuting time is an important indicator for an individual to determine the residence and choose the means of transportation. The analysis uses the data collected from people who live in Seoul metropolitan area including Seoul, Incheon and Gyeonggi Province, and commute to work and making the simple linkage travel (home-work-home) within the area. For the analysis, the Cox hazard proportional methodology was adopted. The method is known to be well applied without assuming any distribution in case of the dependent variable being continuous. For the covariate, the interaction effect between the space variable of the work place and the variable of transportation has been also included in the model. The commuting time to work has been estimated for both 1) the whole metropolitan area and 2) the separate regions i.e., Seoul, Incheon and Gyeonggi-Do. The result reveals that characteristic variables related to individual, household and travel properties influence the mode of transportation and the time allocated for commuting to work (p<0.01). This study also demonstrates the usefulness of the Cox hazard proportional model. The data used in this study is the actual household travel data surveyed in 2006 in the metropolitan area, and analyzing the survey data in 2010 is currently in progress. Comparison of the two survey data sets seeking any behavioral change is suggested for the future study.