Bayesian model averaging for predicting factors associated with length of COVID-19 hospitalization.

Bahrami, Shabnam; Hajian-Tilaki, Karimollah; Bayani, Masomeh; Chehrazi, Mohammad; Mohamadi-Pirouz, Zahra; Amoozadeh, Abazar

Bahrami, Shabnam; Hajian-Tilaki, Karimollah; Bayani, Masomeh; Chehrazi, Mohammad; Mohamadi-Pirouz, Zahra; Amoozadeh, Abazar.

Afiliación

Bahrami S; Student Research Center, Research Institute, Babol University of Medical Sciences, Babol, Iran.
Hajian-Tilaki K; Department of Biostatistics and Epidemiology, School of Public Health, Babol University of Medical Sciences, Babol, Iran. drhajian@yahoo.com.
Bayani M; Social Determinants of Health Research Center, Research Institute, Babol University of Medical Sciences, Babol, Iran. drhajian@yahoo.com.
Chehrazi M; Department of Infectious Diseases, Ayatollah Rohani Hospital, Babol University of Medical Sciences, Babol, Iran.
Mohamadi-Pirouz Z; Department of Biostatistics and Epidemiology, School of Public Health, Babol University of Medical Sciences, Babol, Iran.
Amoozadeh A; Neonatal Research Unit, Imperial College London, Exhibition Rd, South Kensington, London, SW7 2BX, UK.

BMC Med Res Methodol ; 23(1): 163, 2023 07 06.

Article en En | MEDLINE | ID: mdl-37415112

Asunto(s)

COVID-19; Humanos; Estudios de Cohortes; Teorema de Bayes; Hospitalización; Tiempo de Internación; Convulsiones

Palabras clave

AIC; BIC; Bayesian model averaging; COVID-19; GBDT; Length of hospital stay; Markov Chain Monte Carlo (MCMC); Occam's Window; Stepwise

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Texto completo: 1 Bases de datos: MEDLINE Asunto principal: COVID-19 Tipo de estudio: Etiology_studies / Incidence_studies / Observational_studies / Prognostic_studies / Risk_factors_studies Límite: Humans Idioma: En Revista: BMC Med Res Methodol Asunto de la revista: MEDICINA Año: 2023 Tipo del documento: Article País de afiliación: Irán

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