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Methods for Analysis of Big Data.: A Combination of the Lasso Method and the Case-Crossover Design for Investigating Potential Drug Side Effects on Myocardial Infarction
Norwegian University of Science and Technology, Faculty of Information Technology, Mathematics and Electrical Engineering, Department of Mathematical Sciences.
2014 (English)MasteroppgaveStudent thesis
Abstract [en]

The current master thesis was written during the academic year 2013 − 2014 at the Norwegian University of Science and Technology (NTNU). It concerns the im- plementation of the case-crossover design in a combination with the lasso method, for investigating potential effects of a set of drugs on myocardial infarction. The datasets that were used in the analysis were generated based on information about the usage frequencies of the drugs in the period from 2008 to 2012. Furthermore, the thesis provides a brief explanation of how the lasso method can be used in the case of generalized linear models, as well as the case-crossover design. The main analysis was based on two datasets such that probably weak aspects of the lasso could be discovered. Another aspect of the current thesis was the implementation of the relatively new inference method for the lasso, as well as the implementa- tion of two forms of the lasso method: simple lasso and bootstrap lasso. Finally, the current thesis shows, with numerical results, that the bootstrap form of lasso is an effective variable selection method, and that the lasso inference is not yet sufficiently developed.

Place, publisher, year, edition, pages
Institutt for matematiske fag , 2014. , 131 p.
URN: urn:nbn:no:ntnu:diva-25751Local ID: ntnudaim:10627OAI: diva2:740247
Available from: 2014-08-23 Created: 2014-08-23 Last updated: 2014-08-23Bibliographically approved

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