Quantitative Methodologies 2: Confusion bias and how to control a confounding factor

• Autores: Quispe, Antonio M., Alvarez-Valdivia, María Gracia, Loli-Guevara, Silvana
• Formato: artículo
• Fecha de Publicación: 2020
• Institución: Cuerpo Médico Hospital Nacional Almanzor Aguinaga Asenjo
• Repositorio: Revista del Cuerpo Médico Hospital Nacional Almanzor Aguinaga Asenjo
• Lenguaje: español
• OAI Identifier: oai:cmhnaaa_ojs_cmhnaaa.cmhnaaa.org.pe:article/675
• Enlace del recurso: https://cmhnaaa.org.pe/ojs/index.php/rcmhnaaa/article/view/675
• Nivel de acceso: acceso abierto
• Materia: Sesgo de confusión; Diseño de Investigaciones Epidemiológicas; Sesgos; Análisis de regresión; Puntajes de propensión; Confounding Factors; Epidemiologic; Epidemiologic Research Design; Bias; Regression Analysis; Propensity Score

Addressing confounding bias is one of the challenges when conducting causality studies. This occurs when we report a causal association between an exposure and an outcome, when in fact it could be result of the effect of a third factor called confounding variable. That is, when an confounder variable creates a spurious relationship between the exposure or independent variable and the outcome of interest or dependent variable. By knowing the confounding variables and their association with the exposure of interest, the confounding bias could be controlled. To control for confounding bias, we can use different methods. These include techniques applied in study design, such as restriction, randomization, and coincidence, and techniques used in data analysis, such as stratification, multivariate analysis, standardization, propensity scores, analysis sensitivity and the inverse probability weighting. In this review, we discuss how to identify a confounding variable and the main techniques for controlling for confounding bias.