• Title/Summary/Keyword: survival data

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Estimation of Treatment Effect for Bivariate Censored Survival Data

  • Ahn, Choon-Mo;Park, Sang-Gue
    • Communications for Statistical Applications and Methods
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    • v.10 no.3
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    • pp.1017-1024
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    • 2003
  • An estimation problem of treatment effect for bivariate censored survival data is considered under location shift model between two sample. The proposed estimator is very intuitive and can be obtained in a closed form. Asymptotic results of the proposed estimator are discussed and simulation studies are performed to show the strength of the proposed estimator.

Application of a Non-Mixture Cure Rate Model for Analyzing Survival of Patients with Breast Cancer

  • Baghestani, Ahmad Reza;Moghaddam, Sahar Saeedi;Majd, Hamid Alavi;Akbari, Mohammad Esmaeil;Nafissi, Nahid;Gohari, Kimiya
    • Asian Pacific Journal of Cancer Prevention
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    • v.16 no.16
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    • pp.7359-7363
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    • 2015
  • Background: As a result of significant progress made in treatment of many types of cancers during the last few decades, there have been an increased number of patients who do not experience mortality. We refer to these observations as cure or immune and models for survival data which include cure fraction are known as cure rate models or long-term survival models. Materials and Methods: In this study we used the data collected from 438 female patients with breast cancer registered in the Cancer Research Center in Shahid Beheshti University of Medical Sciences, Tehran, Iran. The patients had been diagnosed from 1992 to 2012 and were followed up until October 2014. We had to exclude some because of incomplete information. Phone calls were made to confirm whether the patients were still alive or not. Deaths due to breast cancer were regarded as failure. To identify clinical, pathological, and biological characteristics of patients that might have had an effect on survival of the patients we used a non-mixture cure rate model; in addition, a Weibull distribution was proposed for the survival time. Analyses were performed using STATA version 14. The significance level was set at $P{\leq}0.05$. Results: A total of 75 patients (17.1%) died due to breast cancer during the study, up to the last follow-up. Numbers of metastatic lymph nodes and histologic grade were significant factors. The cure fraction was estimated to be 58%. Conclusions: When a cure fraction is not available, the analysis will be changed to standard approaches of survival analysis; however when the data indicate that the cure fraction is available, we suggest analysis of survival data via cure models.

Genetic Mixed Effects Models for Twin Survival Data

  • Ha, Il-Do;Noh, Maengseok;Yoon, Sangchul
    • Communications for Statistical Applications and Methods
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    • v.12 no.3
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    • pp.759-771
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    • 2005
  • Twin studies are one of the most widely used methods for quantifying the influence of genetic and environmental factors on some traits such as a life span or a disease. In this paper we propose a genetic mixed linear model for twin survival time data, which allows us to separate the genetic component from the environmental component. Inferences are based upon the hierarchical likelihood (h-likelihood), which provides a statistically efficient and simple unified framework for various random-effect models. We also propose a simple and fast computation method for analyzing a large data set on twin survival study. The new method is illustrated to the survival data in Swedish Twin Registry. A simulation study is carried out to evaluate the performance.

The Bivariate Kumaraswamy Weibull regression model: a complete classical and Bayesian analysis

  • Fachini-Gomes, Juliana B.;Ortega, Edwin M.M.;Cordeiro, Gauss M.;Suzuki, Adriano K.
    • Communications for Statistical Applications and Methods
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    • v.25 no.5
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    • pp.523-544
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    • 2018
  • Bivariate distributions play a fundamental role in survival and reliability studies. We consider a regression model for bivariate survival times under right-censored based on the bivariate Kumaraswamy Weibull (Cordeiro et al., Journal of the Franklin Institute, 347, 1399-1429, 2010) distribution to model the dependence of bivariate survival data. We describe some structural properties of the marginal distributions. The method of maximum likelihood and a Bayesian procedure are adopted to estimate the model parameters. We use diagnostic measures based on the local influence and Bayesian case influence diagnostics to detect influential observations in the new model. We also show that the estimates in the bivariate Kumaraswamy Weibull regression model are robust to deal with the presence of outliers in the data. In addition, we use some measures of goodness-of-fit to evaluate the bivariate Kumaraswamy Weibull regression model. The methodology is illustrated by means of a real lifetime data set for kidney patients.

Weighted Estimation of Survival Curves for NBU Class Based on Censored Data

  • Lee, Sang-Bock
    • Journal of the Korean Data and Information Science Society
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    • v.7 no.1
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    • pp.59-68
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    • 1996
  • In this paper, we consider how to estimate New Better Than Used (NBU) survival curves from randomly right censored data. We propose several possible NBU estimators and study their properties. Numerical studies indicate that the proposed estimators are appropriate in practical use. Some useful examples are presented.

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Comparison of missing data methods in clustered survival data using Bayesian adaptive B-Spline estimation

  • Yoo, Hanna;Lee, Jae Won
    • Communications for Statistical Applications and Methods
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    • v.25 no.2
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    • pp.159-172
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    • 2018
  • In many epidemiological studies, missing values in the outcome arise due to censoring. Such censoring is what makes survival analysis special and differentiated from other analytical methods. There are many methods that deal with censored data in survival analysis. However, few studies have dealt with missing covariates in survival data. Furthermore, studies dealing with missing covariates are rare when data are clustered. In this paper, we conducted a simulation study to compare results of several missing data methods when data had clustered multi-structured type with missing covariates. In this study, we modeled unknown baseline hazard and frailty with Bayesian B-Spline to obtain more smooth and accurate estimates. We also used prior information to achieve more accurate results. We assumed the missing mechanism as MAR. We compared the performance of five different missing data techniques and compared these results through simulation studies. We also presented results from a Multi-Center study of Korean IBD patients with Crohn's disease(Lee et al., Journal of the Korean Society of Coloproctology, 28, 188-194, 2012).

Statistical Applications for the Prediction of White Hispanic Breast Cancer Survival

  • Khan, Hafiz Mohammad Rafiqullah;Saxena, Anshul;Gabbidon, Kemesha;Ross, Elizabeth;Shrestha, Alice
    • Asian Pacific Journal of Cancer Prevention
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    • v.15 no.14
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    • pp.5571-5575
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    • 2014
  • Background: The ability to predict the survival time of breast cancer patients is important because of the potential high morbidity and mortality associated with the disease. To develop a predictive inference for determining the survival of breast cancer patients, we applied a novel Bayesian method. In this paper, we propose the development of a databased statistical probability model and application of the Bayesian method to predict future survival times for White Hispanic female breast cancer patients, diagnosed in the US during 1973-2009. Materials and Methods: A stratified random sample of White Hispanic female patient survival data was selected from the Surveillance Epidemiology and End Results (SEER) database to derive statistical probability models. Four were considered to identify the best-fit model. We used three standard model-building criteria, which included Akaike Information Criteria (AIC), Bayesian Information Criteria (BIC), and Deviance Information Criteria (DIC) to measure the goodness of fit. Furthermore, the Bayesian method was used to derive future survival inferences for survival times. Results: The highest number of White Hispanic female breast cancer patients in this sample was from New Mexico and the lowest from Hawaii. The mean (SD) age at diagnosis (years) was 58.2 (14.2). The mean (SD) of survival time (months) for White Hispanic females was 72.7 (32.2). We found that the exponentiated Weibull model best fit the survival times compared to other widely known statistical probability models. The predictive inference for future survival times is presented using the Bayesian method. Conclusions: The findings are significant for treatment planning and health-care cost allocation. They should also contribute to further research on breast cancer survival issues.

Dimension reduction for right-censored survival regression: transformation approach

  • Yoo, Jae Keun;Kim, Sung-Jin;Seo, Bi-Seul;Shin, Hyejung;Sim, Su-Ah
    • Communications for Statistical Applications and Methods
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    • v.23 no.3
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    • pp.259-268
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    • 2016
  • High-dimensional survival data with large numbers of predictors has become more common. The analysis of such data can be facilitated if the dimensions of predictors are adequately reduced. Recent studies show that a method called sliced inverse regression (SIR) is an effective dimension reduction tool in high-dimensional survival regression. However, it faces incapability in implementation due to a double categorization procedure. This problem can be overcome in the right-censoring type by transforming the observed survival time and censoring status into a single variable. This provides more flexibility in the categorization, so the applicability of SIR can be enhanced. Numerical studies show that the proposed transforming approach is equally good to (or even better) than the usual SIR application in both balanced and highly-unbalanced censoring status. The real data example also confirms its practical usefulness, so the proposed approach should be an effective and valuable addition to usual statistical practitioners.

Using fuzzy-neural network to predict hedge fund survival (퍼지신경망 모형을 이용한 헤지펀드의 생존여부 예측)

  • Lee, Kwang Jae;Lee, Hyun Jun;Oh, Kyong Joo
    • Journal of the Korean Data and Information Science Society
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    • v.26 no.6
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    • pp.1189-1198
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    • 2015
  • For the effects of the global financial crisis cause hedge funds to have a strong influence on financial markets, it is needed to study new approach method to predict hedge fund survival. This paper proposes to organize fuzzy neural network using hedge fund data as input to predict hedge fund survival. The variables of hedge fund data are ambiguous to analyze and have internal uncertainty and these characteristics make it challenging to predict their survival from the past records. The object of this study is to evaluate the predictability of fuzzy neural network which uses grades of membership to predict survival. The results of this study show that proposed system is effective to predict the hedge funds survival and can be a desirable solution which helps investors to support decision-making.

Estimation of continuous odds ratio function with censored data (중도절단된 자료를 포함한 승산비 연속함수의 추정)

  • Kim, Jung-Suk;Kwon, Chang-Hee
    • 한국디지털정책학회:학술대회논문집
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    • 2006.12a
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    • pp.327-336
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    • 2006
  • The odds ratio is used for assessing the disease-exposure association, because epidemiological data for case-control of cohort studies are often summarized into 2 ${\times}$ 2 tables. In this paper we define the odds ratio function(ORF) that extends odds ratio used on discrete survival event data to continuous survival time data and propose estimation procedures with censored data. The first one is a nonparametric estimator based on the Nelson-Aalen estimator of comulative hazard function, and the others are obtained using the concept of empirical odds ratio. Asymptotic properties such as consistency and weak convergence results are also provided. The ORF provides a simple interpretation and is comparable to survival function or comulative hazard function in comparing two groups. The mean square errors are investigated via Monte Carlo simulation. The result are finally illustrated using the Melanoma data.

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