TY - JOUR
T1 - Cómo entender las medidas de efecto en la investigación clínica
T2 - Interpretación práctica y aplicación
AU - Zafra-Tanaka, Jessica Hanae
AU - Taype-Rondan, Alvaro
AU - Fernandez-Guzman, Daniel
N1 - Publisher Copyright:
© 2023 Medical Body of the Almanzor Aguinaga Asenjo National Hospital. All rights reserved.
PY - 2023
Y1 - 2023
N2 - In clinical research, assessing the association between two variables is a critical and fundamental task. Clinical studies aim to establish the effect size of the exposure to a variable on a given outcome. To measure this effect size, various statistical measures are used, among the most common are the prevalence ratio (PR), the relative risk (RR), the odds ratio (OR), the hazard ratio (HR), the incidence rate ratio (IRR), the attributable risk (AR), the number needed to treat (NNT), the mean difference (MD), and the linear regression coefficient (β). Each of these measures has its advantages and limitations, and their choice depends on the type of study and the nature of the data being analyzed. Therefore, it is important to understand the interpretation and use of each of them to perform an appropriate analysis. In this article, our goal is to explain in a practical way how to interpret these measures and how to use their p-values and 95% confidence intervals to assess statistical inference. Understanding how to evaluate the association between two variables is crucial for the design and analysis of high-quality clinical studies. This enables evidence-based decision-making and promotes improvements in patient care.
AB - In clinical research, assessing the association between two variables is a critical and fundamental task. Clinical studies aim to establish the effect size of the exposure to a variable on a given outcome. To measure this effect size, various statistical measures are used, among the most common are the prevalence ratio (PR), the relative risk (RR), the odds ratio (OR), the hazard ratio (HR), the incidence rate ratio (IRR), the attributable risk (AR), the number needed to treat (NNT), the mean difference (MD), and the linear regression coefficient (β). Each of these measures has its advantages and limitations, and their choice depends on the type of study and the nature of the data being analyzed. Therefore, it is important to understand the interpretation and use of each of them to perform an appropriate analysis. In this article, our goal is to explain in a practical way how to interpret these measures and how to use their p-values and 95% confidence intervals to assess statistical inference. Understanding how to evaluate the association between two variables is crucial for the design and analysis of high-quality clinical studies. This enables evidence-based decision-making and promotes improvements in patient care.
KW - Hazard Ratio (Source: MeSH-NLM)
KW - Measures of Association
KW - Odds Ratio
KW - Prevalence Ratio
KW - Relative Risk
UR - http://www.scopus.com/inward/record.url?scp=85190269916&partnerID=8YFLogxK
U2 - 10.35434/rcmhnaaa.2023.161.1935
DO - 10.35434/rcmhnaaa.2023.161.1935
M3 - Artículo
AN - SCOPUS:85190269916
SN - 2225-5109
VL - 16
JO - Revista del Cuerpo Medico Hospital Nacional Almanzor Aguinaga Asenjo
JF - Revista del Cuerpo Medico Hospital Nacional Almanzor Aguinaga Asenjo
ER -