Regresiones aplicadas al estudio de eventos discretos en epidemiología
Some basic aspects about using regressions in epidemiological studies are reviewed. Particularly, this manuscript focused on those applied to the study of discrete events. Generalized lineal models, such as Poisson and log-binomial, have a structure that is an extension of a lineal...
- Autores:
- Tipo de recurso:
- Fecha de publicación:
- 2016
- Institución:
- Universidad Industrial de Santander
- Repositorio:
- Repositorio UIS
- Idioma:
- spa
- OAI Identifier:
- oai:noesis.uis.edu.co:20.500.14071/8836
- Acceso en línea:
- https://revistas.uis.edu.co/index.php/revistasaluduis/article/view/5397
https://noesis.uis.edu.co/handle/20.500.14071/8836
- Palabra clave:
- Generalized Lineal Models
Poisson Regression
Binomial Regression
Incidence Rate Ratio
Relative Risk
Prevalence Ratio
Modelos lineales generalizados
Regresión de Poisson
Regresión Binomial
Razón de tasas
Riesgo Relativo
Razón de Prevalencias
- Rights
- openAccess
- License
- Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)
| Summary: | Some basic aspects about using regressions in epidemiological studies are reviewed. Particularly, this manuscript focused on those applied to the study of discrete events. Generalized lineal models, such as Poisson and log-binomial, have a structure that is an extension of a lineal equation to analyze discrete outcomes. Thus, we can estimate association measures as the incidence rate ratio, using the Poisson regression, or the relative risk (or prevalence ratio), using log-binomial regression. In each case it is essential to know the nature of the dependent variable, as well as, its distribution and recognize the limitations of each analysis tool. |
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