Semi-Markov multistate model with interval-censored transition times
A Cox-based multistate model is proposed for analyzing a multicohort event history process with interval-censored transition times. The cohort is included as a stratum variable when modeling each transition hazard, while testing the compliance with the Markov property conditional on the prognostic c...
| Autores: | , , |
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| Tipo de recurso: | capítulo de libro |
| Fecha de publicación: | 2024 |
| 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:2117/415332 |
| Acceso en línea: | https://hdl.handle.net/2117/415332 https://dx.doi.org/10.1007/978-3-031-65723-8_39 |
| Access Level: | acceso abierto |
| Palabra clave: | Markov processes COVID-19 (Disease) -- Mathematical models Semi-Markov multistate model Interval-censored data Multiple imputation COVID-19 Markov, Processos de COVID-19 (Malaltia) -- Models matemàtics Àrees temàtiques de la UPC::Matemàtiques i estadística::Estadística aplicada::Estadística biosanitària |
| Sumario: | A Cox-based multistate model is proposed for analyzing a multicohort event history process with interval-censored transition times. The cohort is included as a stratum variable when modeling each transition hazard, while testing the compliance with the Markov property conditional on the prognostic covariates. Whenever the Markovian assumption does not hold for a given transition, the time of entry into the current state is incorporated in the modeling procedure, yielding a semi-Markov process. To deal with interval censoring, an easy-to-implement procedure is based on performing a multiple imputation of the unknown transition times within the specified intervals. The corresponding artificially completed datasets are separately fitted using the proposed multistate model, each providing inference on the target population quantity. Finally, the overall collected information is properly combined. The described methodology is applied to a three-wave dataset of COVID-19-hospitalized adults. |
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