• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.5
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• pp.581-586
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• 2017

The facility modernization policy has been established to improve fruits quality and to increase fruits yield per acreage. The fruit production quantity of farms joined in the policy was increased. Therefore, many fruit farms want to participate in the policy. The government has subsidized fruit farms to modernize their facilities such as rain proof, drainage way, frost proof, etc. This study analyzes the performance of the facility modernization policy focused on apple, eastern pear, and grape cultivation sector. One hundred apple farms, one hundred eastern pear farms, and 91 grape farms were surveyed. The performance of the policy was reviewed using analytical technique such as Covariate Matching and Propensity Score Matching and several policy implications were suggested.

• Proceedings of the Korean Statistical Society Conference
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• 2003.10a
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• pp.179-182
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• 2003

This study describes a new graphical method for assessing and characterizing effect modification by a matching covariate in matched case-control studies. This method to understand effect modification is based on a semiparametric model using a varying coefficient model. The method allows for nonparametric relationships between effect modification and other covariates, or can be useful in suggesting parametric models. This method can be applied to examining effect modification by any ordered categorical or continuous covariates for which cases have been matched with controls. The method applies to effect modification when causality might be reasonably assumed. An example from veterinary medicine is used to demonstrate our approach. The simulation results show that this method, when based on linear, quadratic and nonparametric effect modification, can be more powerful than both a parametric multiplicative model fit and a fully nonparametric generalized additive model fit.

• 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 the Korean Data and Information Science Society
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• v.26 no.6
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• pp.1317-1325
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• 2015

There are two types of clinical research to figure out risk factor for disease using collected data. One is prospective study to approach the subjects from the present time and the other is retrospective study to find the risk factor using the subject's information in the past. Both approached and study design are different but the purpose of the two studies is to identify a significant difference between two groups and to find out what the variables to influence groups. Especially when comparing the two groups in clinical research, we have to look at the difference between the impact clinical variables by group while controlling the influence of the baseline characteristics variables such as age and sex. However, in the retrospective study, the difference of baseline characteristic variables can occur more frequently because the past records did not randomly assign subjects into two groups. In clinical data analysis use covariates to solve this problem. Typically, the analysis method using the analysis of covariance of variance, adjusted model, and propensity score matching method. This study is introduce the way of equality adjustment between groups data analysis using covariates in retrospective clinical studies and apply it to the recurrence of gastric cancer data.