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http://hdl.handle.net/10609/72586
Title: Missing data analysis in longitudinal data. How to analyze it?
Author: Curto García, Jorge Juan
Director: Sánchez Pla, Alexandre
Tutor: Pérez Álvarez, Nuria
Others: Universitat Oberta de Catalunya
Keywords: longitudinal data
bioinformatics
R programming language
Issue Date: Jan-2018
Publisher: Universitat Oberta de Catalunya
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).
Language: Spanish
URI: http://hdl.handle.net/10609/72586
Appears in Collections:Bachelor thesis, research projects, etc.

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