How to interpret the results of medical time series data analysis: Classical statistical approaches versus dynamic Bayesian network modeling
Background: Classical statistics is a well-established approach in the analysis of medical data. While the medical community seems to be familiar with the concept of a statistical analysis and its interpretation, the Bayesian approach, argued by many of its proponents to be superior to the classical...
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| Format: | Article |
| Language: | English |
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Elsevier
2016-01-01
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| Series: | Journal of Pathology Informatics |
| Subjects: | |
| Online Access: | http://www.jpathinformatics.org/article.asp?issn=2153-3539;year=2016;volume=7;issue=1;spage=50;epage=50;aulast=Onisko |
| _version_ | 1811336238636990464 |
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| author | Agnieszka Onisko Marek J Druzdzel R Marshall Austin |
| author_facet | Agnieszka Onisko Marek J Druzdzel R Marshall Austin |
| author_sort | Agnieszka Onisko |
| collection | DOAJ |
| description | Background: Classical statistics is a well-established approach in the analysis of medical data. While the medical community seems to be familiar with the concept of a statistical analysis and its interpretation, the Bayesian approach, argued by many of its proponents to be superior to the classical frequentist approach, is still not well-recognized in the analysis of medical data. Aim: The goal of this study is to encourage data analysts to use the Bayesian approach, such as modeling with graphical probabilistic networks, as an insightful alternative to classical statistical analysis of medical data. Materials and Methods: This paper offers a comparison of two approaches to analysis of medical time series data: (1) classical statistical approach, such as the Kaplan-Meier estimator and the Cox proportional hazards regression model, and (2) dynamic Bayesian network modeling. Our comparison is based on time series cervical cancer screening data collected at Magee-Womens Hospital, University of Pittsburgh Medical Center over 10 years. Results: The main outcomes of our comparison are cervical cancer risk assessments produced by the three approaches. However, our analysis discusses also several aspects of the comparison, such as modeling assumptions, model building, dealing with incomplete data, individualized risk assessment, results interpretation, and model validation. Conclusion : Our study shows that the Bayesian approach is (1) much more flexible in terms of modeling effort, and (2) it offers an individualized risk assessment, which is more cumbersome for classical statistical approaches. |
| first_indexed | 2024-04-13T17:36:33Z |
| format | Article |
| id | doaj.art-ff4785ec929c49a2a304671f82e7530b |
| institution | Directory Open Access Journal |
| issn | 2153-3539 2153-3539 |
| language | English |
| last_indexed | 2024-04-13T17:36:33Z |
| publishDate | 2016-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Pathology Informatics |
| spelling | doaj.art-ff4785ec929c49a2a304671f82e7530b2022-12-22T02:37:21ZengElsevierJournal of Pathology Informatics2153-35392153-35392016-01-0171505010.4103/2153-3539.197191How to interpret the results of medical time series data analysis: Classical statistical approaches versus dynamic Bayesian network modelingAgnieszka OniskoMarek J DruzdzelR Marshall AustinBackground: Classical statistics is a well-established approach in the analysis of medical data. While the medical community seems to be familiar with the concept of a statistical analysis and its interpretation, the Bayesian approach, argued by many of its proponents to be superior to the classical frequentist approach, is still not well-recognized in the analysis of medical data. Aim: The goal of this study is to encourage data analysts to use the Bayesian approach, such as modeling with graphical probabilistic networks, as an insightful alternative to classical statistical analysis of medical data. Materials and Methods: This paper offers a comparison of two approaches to analysis of medical time series data: (1) classical statistical approach, such as the Kaplan-Meier estimator and the Cox proportional hazards regression model, and (2) dynamic Bayesian network modeling. Our comparison is based on time series cervical cancer screening data collected at Magee-Womens Hospital, University of Pittsburgh Medical Center over 10 years. Results: The main outcomes of our comparison are cervical cancer risk assessments produced by the three approaches. However, our analysis discusses also several aspects of the comparison, such as modeling assumptions, model building, dealing with incomplete data, individualized risk assessment, results interpretation, and model validation. Conclusion : Our study shows that the Bayesian approach is (1) much more flexible in terms of modeling effort, and (2) it offers an individualized risk assessment, which is more cumbersome for classical statistical approaches.http://www.jpathinformatics.org/article.asp?issn=2153-3539;year=2016;volume=7;issue=1;spage=50;epage=50;aulast=OniskoCervical cancer screeningCox proportional hazards regression modeldynamic Bayesian networksKaplan-Meier estimatortime series data |
| spellingShingle | Agnieszka Onisko Marek J Druzdzel R Marshall Austin How to interpret the results of medical time series data analysis: Classical statistical approaches versus dynamic Bayesian network modeling Journal of Pathology Informatics Cervical cancer screening Cox proportional hazards regression model dynamic Bayesian networks Kaplan-Meier estimator time series data |
| title | How to interpret the results of medical time series data analysis: Classical statistical approaches versus dynamic Bayesian network modeling |
| title_full | How to interpret the results of medical time series data analysis: Classical statistical approaches versus dynamic Bayesian network modeling |
| title_fullStr | How to interpret the results of medical time series data analysis: Classical statistical approaches versus dynamic Bayesian network modeling |
| title_full_unstemmed | How to interpret the results of medical time series data analysis: Classical statistical approaches versus dynamic Bayesian network modeling |
| title_short | How to interpret the results of medical time series data analysis: Classical statistical approaches versus dynamic Bayesian network modeling |
| title_sort | how to interpret the results of medical time series data analysis classical statistical approaches versus dynamic bayesian network modeling |
| topic | Cervical cancer screening Cox proportional hazards regression model dynamic Bayesian networks Kaplan-Meier estimator time series data |
| url | http://www.jpathinformatics.org/article.asp?issn=2153-3539;year=2016;volume=7;issue=1;spage=50;epage=50;aulast=Onisko |
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