Journal of Pathology Informatics Journal of Pathology Informatics
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ORIGINAL ARTICLE
Year : 2016  |  Volume : 7  |  Issue : 1  |  Page : 50

How to interpret the results of medical time series data analysis: Classical statistical approaches versus dynamic Bayesian network modeling


1 Department of Pathology, University of Pittsburgh Medical Center, Magee-Womens Hospital, Pittsburgh, PA 15213, USA; Faculty of Computer Science, Bialystok University of Technology, 15-351 Bialystok, Poland
2 Faculty of Computer Science, Bialystok University of Technology, 15-351 Bialystok, Poland; Decision Systems Laboratory, School of Information Sciences and Intelligent Systems Program, University of Pittsburgh, Pittsburgh, PA 15260, USA
3 Department of Pathology, University of Pittsburgh Medical Center, Magee-Womens Hospital, Pittsburgh, PA 15213, USA

Correspondence Address:
Agnieszka Onisko
Department of Pathology, University of Pittsburgh Medical Center, Magee-Womens Hospital, Pittsburgh, PA 15213, USA; Faculty of Computer Science, Bialystok University of Technology, 15-351 Bialystok, Poland

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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/2153-3539.197191

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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.


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