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,...
| Autores: | , , |
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| 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 |
| Sumario: | 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. |
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