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Visualising disease progression on multiple variables with vector plots and path plots
BACKGROUND: It is often desirable to observe how a disease progresses over time in individual patients, rather than graphing group averages; and since multiple outcomes are typically recorded on each patient, it would be advantageous to visualise disease progression on multiple variables simultaneou...
Autores principales: | , , , |
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Formato: | Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2009
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2693505/ https://www.ncbi.nlm.nih.gov/pubmed/19473528 http://dx.doi.org/10.1186/1471-2288-9-32 |
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author | Lazic, Stanley E Mason, Sarah L Michell, Andrew W Barker, Roger A |
author_facet | Lazic, Stanley E Mason, Sarah L Michell, Andrew W Barker, Roger A |
author_sort | Lazic, Stanley E |
collection | PubMed |
description | BACKGROUND: It is often desirable to observe how a disease progresses over time in individual patients, rather than graphing group averages; and since multiple outcomes are typically recorded on each patient, it would be advantageous to visualise disease progression on multiple variables simultaneously. METHODS: A variety of vector plots and a path plot have been developed for this purpose, and data from a longitudinal Huntington's disease study are used to illustrate the utility of these graphical methods for exploratory data analysis. RESULTS: Initial and final values for three outcome variables can be easily visualised per patient, along with the change in these variables over time. In addition to the disease trajectory, the path individual patients take from initial to final observation can be traced. Categorical variables can be coded with different types of vectors or paths (e.g. different colours, line types, line thickness) and separate panels can be used to include further categorical or continuous variables, allowing clear visualisation of further information for each individual. In addition, summary statistics such as mean vectors, bivariate interquartile ranges and convex polygons can be included to assist in interpreting trajectories, comparing groups, and detecting multivariate outliers. CONCLUSION: Vector and path plots are useful graphical methods for exploratory data analysis when individual-level information on multiple variables over time is desired, and they have several advantages over plotting each variable separately. |
format | Text |
id | pubmed-2693505 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2009 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-26935052009-06-08 Visualising disease progression on multiple variables with vector plots and path plots Lazic, Stanley E Mason, Sarah L Michell, Andrew W Barker, Roger A BMC Med Res Methodol Research Article BACKGROUND: It is often desirable to observe how a disease progresses over time in individual patients, rather than graphing group averages; and since multiple outcomes are typically recorded on each patient, it would be advantageous to visualise disease progression on multiple variables simultaneously. METHODS: A variety of vector plots and a path plot have been developed for this purpose, and data from a longitudinal Huntington's disease study are used to illustrate the utility of these graphical methods for exploratory data analysis. RESULTS: Initial and final values for three outcome variables can be easily visualised per patient, along with the change in these variables over time. In addition to the disease trajectory, the path individual patients take from initial to final observation can be traced. Categorical variables can be coded with different types of vectors or paths (e.g. different colours, line types, line thickness) and separate panels can be used to include further categorical or continuous variables, allowing clear visualisation of further information for each individual. In addition, summary statistics such as mean vectors, bivariate interquartile ranges and convex polygons can be included to assist in interpreting trajectories, comparing groups, and detecting multivariate outliers. CONCLUSION: Vector and path plots are useful graphical methods for exploratory data analysis when individual-level information on multiple variables over time is desired, and they have several advantages over plotting each variable separately. BioMed Central 2009-05-27 /pmc/articles/PMC2693505/ /pubmed/19473528 http://dx.doi.org/10.1186/1471-2288-9-32 Text en Copyright ©2009 Lazic et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Lazic, Stanley E Mason, Sarah L Michell, Andrew W Barker, Roger A Visualising disease progression on multiple variables with vector plots and path plots |
title | Visualising disease progression on multiple variables with vector plots and path plots |
title_full | Visualising disease progression on multiple variables with vector plots and path plots |
title_fullStr | Visualising disease progression on multiple variables with vector plots and path plots |
title_full_unstemmed | Visualising disease progression on multiple variables with vector plots and path plots |
title_short | Visualising disease progression on multiple variables with vector plots and path plots |
title_sort | visualising disease progression on multiple variables with vector plots and path plots |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2693505/ https://www.ncbi.nlm.nih.gov/pubmed/19473528 http://dx.doi.org/10.1186/1471-2288-9-32 |
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