Please use this identifier to cite or link to this item: http://hdl.handle.net/10609/72586
Title: Missing data analysis in longitudinal data. How to analyze it?
Author: Curto García, Jorge Juan
Universitat Oberta de Catalunya
Sánchez-Pla, Alex  
Perez-Alvarez, Nuria  
Abstract: In this work, we intend to characterize the studies with longitudinal data and the problems derived from the analyzes in which missing data are presented. In recent years, based on the great advances in computational capacity that allow the application of more complex algorithms, there have been developed new methods of processing missing data in the context of longitudinal data analysis. The aim of this work is to investigate the different types of missing data and the available methodology to address their analysis in the longitudinal data field, in order to identify benefits and limitations of these methods. In the final phase of the work, an exemplification of the application of the methods studied will be presented through the analysis of a longitudinal database in the field of biomedicine, generating a dynamic statistical report (using free license software: R and Markdown).
Keywords: longitudinal data
bioinformatics
R programming language
Type: info:eu-repo/semantics/masterThesis
Issue Date: Jan-2018
Appears in Collections:Trabajos finales de carrera, trabajos de investigación, etc.

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