Ανάλυση Επιδημιολογικών Δεδομένων 
με χρήση της R

Βασιλόπουλος, Νοταράς; Vasilopoulos, Notaras

dc.contributor.author	Βασιλόπουλος, Νοταράς	el
dc.contributor.author	Vasilopoulos, Notaras	en
dc.date.accessioned	2020-12-04T17:12:35Z
dc.date.available	2020-12-04T17:12:35Z
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/52272
dc.identifier.uri	http://dx.doi.org/10.26240/heal.ntua.19970
dc.description	Εθνικό Μετσόβιο Πολυτεχνείο--Μεταπτυχιακή Εργασία. Διεπιστημονικό-Διατμηματικό Πρόγραμμα Μεταπτυχιακών Σπουδών (Δ.Π.Μ.Σ.) “Εφαρμοσμένες Μαθηματικές Επιστήμες”	el
dc.rights	Default License
dc.subject	Λογιστική Παλινδρόμηση	el
dc.subject	Ποινικοποιημένη Λογιστική Παλινδρόμηση	el
dc.subject	Ανάλυση Επιβίωσης	el
dc.subject	Ανάλυση Επιδημιολογικών Δεδομένων	el
dc.subject	Logistic Regression	en
dc.subject	Penalized Logistic Regression	en
dc.subject	Survival Analysis	en
dc.subject	Competing Risks Survival Analysis	en
dc.subject	Epidemiological Analysis	en
dc.title	Ανάλυση Επιδημιολογικών Δεδομένων με χρήση της R	el
dc.title	Epidemiological Analysis via R
heal.type	masterThesis	el
heal.classification	Mathematics - Statistical Analysis	en
heal.language	el	el
heal.language	en	el
heal.access	campus	el
heal.recordProvider	ntua	el
heal.publicationDate	2020-09-25
heal.abstract	This thesis aims to present the epidemiological analysis via the programming language R. More specifically, In the first chapter, we present the definitions of Biostatistics and Epidemiology along with a brief historical review and the objectives of the science of Epidemiology. We introduce the principles of Epidemiology such as Morbidity Indices, Risk Measures and methods of evaluation Epidemiological Tests such as the ROC curves and the Kappa Index / Statistic and we finally mention the types of Epidemiological Studies along with their respective categories. In the second chapter, we introduce the programming language R, presenting its benefits in the field of Epidemiology. We present the basic ways of importing, managing and clearing data sets. Furthermore, we present the capabilities of R as a tool in descriptive Epidemiology by calculating Statistics of location and variation of a statistical sample, by calculating Risk Measures, constructing Confidence Intervals and performing the corresponding Hypothesis Tests. Finally, we present the capabilities of R in data visualization by constructing graphs. In the third chapter, we introduce the notions of the Logistic Regression model as a Generalized Linear model and we present the basic concepts of the Penalized Logistic Regression. In addition, we implement the Logistic Regression model as well as the Penalized Logistic Regression methods, to the “Framingham” study data set, via R. “Framingham” study data set refers to the risk of coronary heart disease. This specific section aims to decide which variables are the statistically significant ones and which of them affect significantly the occurrence of the risk among the population that is under study. Finally, we reach the optimal model for our data by conducting a series of statistical tests. In the fourth and final chapter, we present the basic notions of the Survival Analysis field and the basic concepts of the Competing Risks Survival Analysis. Furthermore, we implement via R, Survival Analysis techniques, such as Kaplan – Meier estimator, in order to estimate the survival time of a group of patients that suffer from melanoma, during their recovery after the removal surgery. Afterwards, we implement the Cox Proportional – Hazards model via R, in order to decide which variables are the statistically significant ones and which of them affect significantly the survival time of the patients and we reach the optimal model for our data by conducting a series of statistical tests. Finally, we implement Competing Risks Survival Analysis techniques via special packages provided by R and compare these results with the ones we previously received from the Survival Analysis without the existence of competing risks.	en
heal.advisorName	Βοντά, Φιλία	el
heal.committeeMemberName	Βοντά, Φιλία
heal.committeeMemberName	Καραγρηγορίου, Αλέξανδρος	el
heal.committeeMemberName	Καρώνη, Χρυσηίς	el
heal.academicPublisher	Εθνικό Μετσόβιο Πολυτεχνείο. Σχολή Εφαρμοσμένων Μαθηματικών και Φυσικών Επιστημών	el
heal.academicPublisherID	ntua
heal.numberOfPages	196 σ.
heal.fullTextAvailability	false