Overview of Frequentist and Bayesian approach to Survival Analysis
Survival analysis is one of the main areas of focus in medical research in recent years. Survival analysis involves the concept of 'Time to event'. The event may be mortality, onset of disease, response to treatment etc. Purpose of this paper is to provide overview of frequentist and Bayes...
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| Format: | Article |
| Language: | English |
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Iuliu Hatieganu University of Medicine and Pharmacy, Cluj-Napoca
2016-03-01
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| Series: | Applied Medical Informatics |
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| Online Access: | http://ami.info.umfcluj.ro/index.php/AMI/article/view/572 |
| _version_ | 1818313390959886336 |
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| author | Vinaitheerthan RENGANATHAN |
| author_facet | Vinaitheerthan RENGANATHAN |
| author_sort | Vinaitheerthan RENGANATHAN |
| collection | DOAJ |
| description | Survival analysis is one of the main areas of focus in medical research in recent years. Survival analysis involves the concept of 'Time to event'. The event may be mortality, onset of disease, response to treatment etc. Purpose of this paper is to provide overview of frequentist and Bayesian Approaches to Survival Analysis. The paper starts with the overview of the basic concepts of survival analysis and then discusses the frequentist and Bayesian approaches to survival analysis in the biomedical domain with help of hypothetical survival dataset. The survival analysis of the hypothetical data sets showed that for the specific dataset and specific hypothesis, Bayesian approach provided direct probability that the null hypothesis is true or not and the probability that the unknown parameter (mean survival time) lies in a given credible interval wherein the frequentist approach provided p-values and confidence interval for interpreting whether the null hypothesis is true or not and the percentage of intervals which will contain the parameter when the experiment is repeated under same condition. The use of Bayesian survival analysis in biomedical domain has increased due to the availability of advanced commercial and free software, its ability to handle design and analysis issues in survival model and the ease of interpretation of the research findings. |
| first_indexed | 2024-12-13T08:33:00Z |
| format | Article |
| id | doaj.art-d04f6d6f7de14fcf9e33517962edb490 |
| institution | Directory Open Access Journal |
| issn | 1224-5593 2067-7855 |
| language | English |
| last_indexed | 2024-12-13T08:33:00Z |
| publishDate | 2016-03-01 |
| publisher | Iuliu Hatieganu University of Medicine and Pharmacy, Cluj-Napoca |
| record_format | Article |
| series | Applied Medical Informatics |
| spelling | doaj.art-d04f6d6f7de14fcf9e33517962edb4902022-12-21T23:53:44ZengIuliu Hatieganu University of Medicine and Pharmacy, Cluj-NapocaApplied Medical Informatics1224-55932067-78552016-03-013812538Overview of Frequentist and Bayesian approach to Survival AnalysisVinaitheerthan RENGANATHAN0Institutional Research, Skyline University College, P.O. Box 1797, University City, Sharjah, United Arab EmiratesSurvival analysis is one of the main areas of focus in medical research in recent years. Survival analysis involves the concept of 'Time to event'. The event may be mortality, onset of disease, response to treatment etc. Purpose of this paper is to provide overview of frequentist and Bayesian Approaches to Survival Analysis. The paper starts with the overview of the basic concepts of survival analysis and then discusses the frequentist and Bayesian approaches to survival analysis in the biomedical domain with help of hypothetical survival dataset. The survival analysis of the hypothetical data sets showed that for the specific dataset and specific hypothesis, Bayesian approach provided direct probability that the null hypothesis is true or not and the probability that the unknown parameter (mean survival time) lies in a given credible interval wherein the frequentist approach provided p-values and confidence interval for interpreting whether the null hypothesis is true or not and the percentage of intervals which will contain the parameter when the experiment is repeated under same condition. The use of Bayesian survival analysis in biomedical domain has increased due to the availability of advanced commercial and free software, its ability to handle design and analysis issues in survival model and the ease of interpretation of the research findings.http://ami.info.umfcluj.ro/index.php/AMI/article/view/572Survival AnalysisBayesianNon-parametric MethodSemi-parametric MethodParametric Method |
| spellingShingle | Vinaitheerthan RENGANATHAN Overview of Frequentist and Bayesian approach to Survival Analysis Applied Medical Informatics Survival Analysis Bayesian Non-parametric Method Semi-parametric Method Parametric Method |
| title | Overview of Frequentist and Bayesian approach to Survival Analysis |
| title_full | Overview of Frequentist and Bayesian approach to Survival Analysis |
| title_fullStr | Overview of Frequentist and Bayesian approach to Survival Analysis |
| title_full_unstemmed | Overview of Frequentist and Bayesian approach to Survival Analysis |
| title_short | Overview of Frequentist and Bayesian approach to Survival Analysis |
| title_sort | overview of frequentist and bayesian approach to survival analysis |
| topic | Survival Analysis Bayesian Non-parametric Method Semi-parametric Method Parametric Method |
| url | http://ami.info.umfcluj.ro/index.php/AMI/article/view/572 |
| work_keys_str_mv | AT vinaitheerthanrenganathan overviewoffrequentistandbayesianapproachtosurvivalanalysis |