MSMpred: interactive modelling and prediction of individual evolution via multistate models

Background: Modelling the course of a disease regarding severe events and identifying prognostic factors is of great clinical relevance. Multistate models (MSM) can be used to describe diseases or processes that change over time using different states and the transitions between them. Specifically,...

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Autores: Garmendia Bergés, Leire|||0000-0002-2053-9535, Cortés Martínez, Jordi|||0000-0002-3764-0795, Gómez Melis, Guadalupe|||0000-0003-4252-4884
Tipo de recurso: artículo
Fecha de publicación:2023
País:España
Institución:Universitat Politècnica de Catalunya (UPC)
Repositorio:UPCommons. Portal del coneixement obert de la UPC
Idioma:inglés
OAI Identifier:oai:upcommons.upc.edu:2117/392152
Acceso en línea:https://hdl.handle.net/2117/392152
https://dx.doi.org/10.1186/s12874-023-01951-3
Access Level:acceso abierto
Palabra clave:Biomathematics
COVID-19 (Disease)
Shiny app
Multistate models
COVID-19
Biomatemàtica
COVID-19 (Malaltia)
Classificació AMS::92 Biology and other natural sciences::92B Mathematical biology in general
Àrees temàtiques de la UPC::Matemàtiques i estadística::Estadística aplicada::Estadística biosanitària
Àrees temàtiques de la UPC::Matemàtiques i estadística::Matemàtica aplicada a les ciències
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network_name_str España
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spelling MSMpred: interactive modelling and prediction of individual evolution via multistate modelsGarmendia Bergés, Leire|||0000-0002-2053-9535Cortés Martínez, Jordi|||0000-0002-3764-0795Gómez Melis, Guadalupe|||0000-0003-4252-4884BiomathematicsCOVID-19 (Disease)Shiny appMultistate modelsCOVID-19BiomatemàticaCOVID-19 (Malaltia)Classificació AMS::92 Biology and other natural sciences::92B Mathematical biology in generalÀrees temàtiques de la UPC::Matemàtiques i estadística::Estadística aplicada::Estadística biosanitàriaÀrees temàtiques de la UPC::Matemàtiques i estadística::Matemàtica aplicada a les ciènciesBackground: Modelling the course of a disease regarding severe events and identifying prognostic factors is of great clinical relevance. Multistate models (MSM) can be used to describe diseases or processes that change over time using different states and the transitions between them. Specifically, they are useful to analyse a disease with an increasing degree of severity, that may precede death. The complexity of these models changes depending on the number of states and transitions taken into account. Due to that, a web tool has been developed making it easier to work with those models. Results: MSMpred is a web tool created with the shiny R package that has two main features: 1) to allow fitting an MSM from specific data; 2) to predict the clinical evolution for a given subject. To fit the model, the data to be analysed must be uploaded in a prespecified format. Then, the user has to define the states and transitions as well as the covariates (e.g., age or gender) involved in each transition. From this information, the app returns histograms or barplots, as appropriate, to represent the distributions of the selected covariates and boxplots to show the patient’s length of stay (for uncensored data) in each state. To make predictions, the values of selected covariates from a new subject at baseline has to be provided. From these inputs, the app provides some indicators of the subject’s evolution, such as the probability of 30-day death or the most likely state at a fixed time. Furthermore, visual representations (e.g., the stacked transition probabilities plot) are given to make predictions more understandable. Conclusions: MSMpred is an intuitive and visual app that eases the work of biostatisticians and facilitates to the medical personnel the interpretation of MSMs.This research was funded by the Ministerio de Ciencia e Innovación (Spain) [PID2019-104830RB-I00/ DOI (AEI): 10.13039/501100011033] and by Generalitat de Catalunya (2020PANDE00148).Peer ReviewedSpringer Nature20232023-05-2420232023-07-25journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/2117/392152https://dx.doi.org/10.1186/s12874-023-01951-3reponame:UPCommons. Portal del coneixement obert de la UPCinstname:Universitat Politècnica de Catalunya (UPC)InglésengAgencia Estatal de Investigación http://doi.org/10.13039/501100011033 Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020 PID2019-104830RB-I00 METODOLOGIAS ESTADISTICAS PARA DATOS CLINICOS Y OMICOS Y SUS APLICACIONES EN CIENCIAS DE LA SALUDopen accesshttp://purl.org/coar/access_right/c_abf2Attribution 4.0 Internationalhttps://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:upcommons.upc.edu:2117/3921522026-05-27T15:37:01Z
dc.title.none.fl_str_mv MSMpred: interactive modelling and prediction of individual evolution via multistate models
title MSMpred: interactive modelling and prediction of individual evolution via multistate models
spellingShingle MSMpred: interactive modelling and prediction of individual evolution via multistate models
Garmendia Bergés, Leire|||0000-0002-2053-9535
Biomathematics
COVID-19 (Disease)
Shiny app
Multistate models
COVID-19
Biomatemàtica
COVID-19 (Malaltia)
Classificació AMS::92 Biology and other natural sciences::92B Mathematical biology in general
Àrees temàtiques de la UPC::Matemàtiques i estadística::Estadística aplicada::Estadística biosanitària
Àrees temàtiques de la UPC::Matemàtiques i estadística::Matemàtica aplicada a les ciències
title_short MSMpred: interactive modelling and prediction of individual evolution via multistate models
title_full MSMpred: interactive modelling and prediction of individual evolution via multistate models
title_fullStr MSMpred: interactive modelling and prediction of individual evolution via multistate models
title_full_unstemmed MSMpred: interactive modelling and prediction of individual evolution via multistate models
title_sort MSMpred: interactive modelling and prediction of individual evolution via multistate models
dc.creator.none.fl_str_mv Garmendia Bergés, Leire|||0000-0002-2053-9535
Cortés Martínez, Jordi|||0000-0002-3764-0795
Gómez Melis, Guadalupe|||0000-0003-4252-4884
author Garmendia Bergés, Leire|||0000-0002-2053-9535
author_facet Garmendia Bergés, Leire|||0000-0002-2053-9535
Cortés Martínez, Jordi|||0000-0002-3764-0795
Gómez Melis, Guadalupe|||0000-0003-4252-4884
author_role author
author2 Cortés Martínez, Jordi|||0000-0002-3764-0795
Gómez Melis, Guadalupe|||0000-0003-4252-4884
author2_role author
author
dc.subject.none.fl_str_mv Biomathematics
COVID-19 (Disease)
Shiny app
Multistate models
COVID-19
Biomatemàtica
COVID-19 (Malaltia)
Classificació AMS::92 Biology and other natural sciences::92B Mathematical biology in general
Àrees temàtiques de la UPC::Matemàtiques i estadística::Estadística aplicada::Estadística biosanitària
Àrees temàtiques de la UPC::Matemàtiques i estadística::Matemàtica aplicada a les ciències
topic Biomathematics
COVID-19 (Disease)
Shiny app
Multistate models
COVID-19
Biomatemàtica
COVID-19 (Malaltia)
Classificació AMS::92 Biology and other natural sciences::92B Mathematical biology in general
Àrees temàtiques de la UPC::Matemàtiques i estadística::Estadística aplicada::Estadística biosanitària
Àrees temàtiques de la UPC::Matemàtiques i estadística::Matemàtica aplicada a les ciències
description Background: Modelling the course of a disease regarding severe events and identifying prognostic factors is of great clinical relevance. Multistate models (MSM) can be used to describe diseases or processes that change over time using different states and the transitions between them. Specifically, they are useful to analyse a disease with an increasing degree of severity, that may precede death. The complexity of these models changes depending on the number of states and transitions taken into account. Due to that, a web tool has been developed making it easier to work with those models. Results: MSMpred is a web tool created with the shiny R package that has two main features: 1) to allow fitting an MSM from specific data; 2) to predict the clinical evolution for a given subject. To fit the model, the data to be analysed must be uploaded in a prespecified format. Then, the user has to define the states and transitions as well as the covariates (e.g., age or gender) involved in each transition. From this information, the app returns histograms or barplots, as appropriate, to represent the distributions of the selected covariates and boxplots to show the patient’s length of stay (for uncensored data) in each state. To make predictions, the values of selected covariates from a new subject at baseline has to be provided. From these inputs, the app provides some indicators of the subject’s evolution, such as the probability of 30-day death or the most likely state at a fixed time. Furthermore, visual representations (e.g., the stacked transition probabilities plot) are given to make predictions more understandable. Conclusions: MSMpred is an intuitive and visual app that eases the work of biostatisticians and facilitates to the medical personnel the interpretation of MSMs.
publishDate 2023
dc.date.none.fl_str_mv 2023
2023-05-24
2023
2023-07-25
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/2117/392152
https://dx.doi.org/10.1186/s12874-023-01951-3
url https://hdl.handle.net/2117/392152
https://dx.doi.org/10.1186/s12874-023-01951-3
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.relation.none.fl_str_mv Agencia Estatal de Investigación http://doi.org/10.13039/501100011033 Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020 PID2019-104830RB-I00 METODOLOGIAS ESTADISTICAS PARA DATOS CLINICOS Y OMICOS Y SUS APLICACIONES EN CIENCIAS DE LA SALUD
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Springer Nature
publisher.none.fl_str_mv Springer Nature
dc.source.none.fl_str_mv reponame:UPCommons. Portal del coneixement obert de la UPC
instname:Universitat Politècnica de Catalunya (UPC)
instname_str Universitat Politècnica de Catalunya (UPC)
reponame_str UPCommons. Portal del coneixement obert de la UPC
collection UPCommons. Portal del coneixement obert de la UPC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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