• The Korean Journal of Applied Statistics
• /
• v.10 no.1
• /
• pp.85-104
• /
• 1997

Proportional hazards model has been widely used for analyzing survival data. This article reviews some well-known goodness-of-fit tests for proportional hazards model. Simulation studies also provide some insights into the properties of these test statistics across several types of survival distributions and degerees of censorship.

• Asian Pacific Journal of Cancer Prevention
• /
• v.16 no.12
• /
• pp.5081-5084
• /
• 2015

Background: Breast cancer is a malignant tumor that starts from cells of the breast and is seen mainly in women. It's the most common cancer in women worldwide and is a major threat to health. The purpose of this study was to fit a Cox proportional hazards model for prediction and determination of years of survival in Iranian patients. Materials and Methods: A total of 366 patients with breast cancer in the Cancer Research Center were included in the study. A Cox proportional hazard model was used with variables such as tumor grade, number of removed positive lymph nodes, human epidermal growth factor receptor 2 (HER2) expression and several other variables. Kaplan-Meier curves were plotted and multi-years of survival were evaluated. Results: The mean age of patients was 48.1 years. Consumption of fatty foods (p=0.033), recurrence (p<0.001), tumor grade (p=0.046) and age (p=0.017) were significant variables. The overall 1- year, 3-year and 5-year survival rates were found to be 93%, 75% and 52%. Conclusions: Use of covariates and the Cox proportional hazard model are effective in predicting the survival of individuals and this model distinguished 4 effective factors in the survival of patients.

• The Journal of Korean Society for School & Community Health Education
• /
• v.23 no.4
• /
• pp.81-93
• /
• 2022

Objectives: The purpose of this study was to develop a model for comprehensively evaluating the risk of sarcopenia in Korean adults and to generate the sarcopenia risk scorecard model based on the results. Methods: The participants of the study were 7,118 adults without sarcopenia in the first basic survey, and a longitudinal analysis was conducted using data from the 1st to 8th survey (2006-2020) of the Korean Longitudinal Study of Aging (KLoSA). The data were analyzed using Rao-Scott chi-square test and weighted Cox proportional hazards regression of complex sampling design. The sarcopenia risk scorecard model was developed by Cox proportional hazards regression using points to double the odds (PDO) method. Results: The findings show that the risk factors for sarcopenia in Korean adults were gender, age, marital status, socioeconomic status, body mass index (BMI), regular exercise, diabetes and arthritis diagnosis. In the scorecard results, the case of exposure to the highest risk level was 100 points. The highest score range were given in the order of age over 65, low BMI, and low socioeconomic status. Conclusions: The significance of this study is that the causal relationship between various factors and the occurrence of sarcopenia in Korean adults was identified. Also, the model developed in this study is expected to be useful in detecting participants with risk of sarcopenia in the community early and preventing and managing sarcopenia through appropriate health education.

• Journal of the Korean Data and Information Science Society
• /
• v.15 no.3
• /
• pp.605-616
• /
• 2004

In this paper we consider the proportional hazard models for survival analysis in the microarray data. For a given vector of response values and gene expressions (covariates), we address the issue of how to reduce the dimension by selecting the significant genes. In our approach, rather than fixing the number of selected genes, we will assign a prior distribution to this number. To implement our methodology, we use a Markov Chain Monte Carlo (MCMC) method.

• 한국데이터정보과학회:학술대회논문집
• /
• 2001.10a
• /
• pp.42-42
• /
• 2001

• The Korean Journal of Applied Statistics
• /
• v.31 no.6
• /
• pp.761-769
• /
• 2018

Simulations are important for survival analyses that deal with censored data. Cox models are widely used in survival analyses, therefore, we investigate how to generate censored data that can simulate the Cox model. Bender et al. (Statistics in Medicine, 24, 1713-1723, 2005) provided a parametric method for generating survival times, but we need to generate censoring times as well as survival times to simulate the censored data. In addition to the parametric method for generating censored data, a nonparametric method is also proposed and applied to a real data set.

• Proceedings of the KSME Conference
• /
• 2008.11a
• /
• pp.1387-1390
• /
• 2008

Pneumatic cylinder is widely used in the various industrial fields. Reliability Study of this field is very important part to the related companies. In this study, we want to predict the life of pneumatic cylinder using Cox (or proportional hazards) model. Used in biomedical applications, the Cox model can be used as an accelerated life testing model. We considered working pressure and temperature as stress factors. The statistical software is used to analyze and forecast the life data.

• Asian Pacific Journal of Cancer Prevention
• /
• v.14 no.11
• /
• pp.6751-6755
• /
• 2013

Background: Research on cancers with a high rate of mortality such as those occurring in the stomach requires using models which can provide a closer examination of disease processes and provide researchers with more accurate data. Various models have been designed based on this issue and the present study aimed at evaluating such models. Materials and Methods: Data from 330 patients with gastric cancer undergoing surgery at Iran Cancer Institute from 1995 to 1999 were analyzed. Cox-Snell Residuals and Akaike Information Criterion were used to compare parametric and semi-parametric Cox models in modeling transition rates among different states of a multi-state model. R 2.15.1 software was used for all data analyses. Results: Analysis of Cox-Snell Residuals and Akaike Information Criterion for all probable transitions among different states revealed that parametric models represented a better fitness. Log-logistic, Gompertz and Log-normal models were good choices for modeling transition rate for relapse hazard (state $1{\rightarrow}state$ 2), death hazard without a relapse (state $1{\rightarrow}state$ 3) and death hazard with a relapse (state $2{\rightarrow}state$ 3), respectively. Conclusions: Although the semi-parametric Cox model is often used by most cancer researchers in modeling transition rates of multistate models, parametric models in similar situations- as they do not need proportional hazards assumption and consider a specific statistical distribution for time to occurrence of next state in case this assumption is not made - are more credible alternatives.

• Proceedings of the Korean Reliability Society Conference
• /
• 2005.06a
• /
• pp.265-269
• /
• 2005

Cox's proportional hazards model (PHM) has been widely applied in the analysis of lifetime data, and it can be characterized by the baseline hazard function and covariates influencing systems' lifetime, where the covariates describe operating environments (e.g. temperature, pressure, humidity). In this article, we consider the constant baseline hazard function and a discrete random variable of a covariate. The estimation procedure is developed in a parametric framework when there are not only complete data but also incomplete one. The Expectation-Maximization (EM) algorithm is employed to handle the incomplete data problem. Simulation results are presented to illustrate the accuracy and some properties of the estimation results.

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