Berea-Baltierra R, Rivas-Ruiz R, Pérez-Rodríguez M, Palacios-Cruz L, Moreno J, Talavera JO

Language: Spanish
References: 9
Page: 192-197
PDF size: 334.67 Kb.

ABSTRACT

The complexity of the causality phenomenon in clinical practice implies that the result of a maneuver is not solely caused by the maneuver, but by the interaction among the maneuver and other baseline factors or variables occurring during the maneuver. This requires methodological designs that allow the evaluation of these variables. When the outcome is a binary variable, we use the multiple logistic regression model (MLRM). This multivariate model is useful when we want to predict or explain, adjusting due to the effect of several risk factors, the effect of a maneuver or exposition over the outcome. In order to perform an MLRM, the outcome or dependent variable must be a binary variable and both categories must mutually exclude each other (i.e. live/death, healthy/ill); on the other hand, independent variables or risk factors may be either qualitative or quantitative. The effect measure obtained from this model is the odds ratio (OR) with 95 % confi dence intervals (CI), from which we can estimate the proportion of the outcome’s variability explained through the risk factors. For these reasons, the MLRM is used in clinical research, since one of the main objectives in clinical practice comprises the ability to predict or explain an event where different risk or prognostic factors are taken into account.

REFERENCES

1. Portney LG, Watkins MP. Logistic regression. En: Foundations of clinical research applications to practice. Third edition. New Jersey, USA: Pearson & Prentice Hall; 2009. p. 696-700.

2. Palacios-Cruz L, Pérez M, Rivas-Ruiz R, Talavera JO. Investigación clínica XVIII. Del juicio clínico al modelo de regresión lineal. Rev Med Inst Mex Seguro Soc. 2013;51(6):656-61.

3. Peduzzi P, Concato J, Kemper E, Holford TR, Feinstein AR. A simulation study of the number of events per variable in logistic regression analysis. J Clin Epidemiol. 1996;49(12):1373-9.

4. Dawson B, Trapp RG. Bioestadística médica. México: Manual Moderno; 2005. p. 239-41.

5. Talavera JO, Rivas-Ruiz R, Pérez-Rodríguez M. Investigación clínica VI. Relevancia clínica. Rev Med Inst Mex Seguro Soc. 2011;49(6):631-5.

6. Feinstein AR. Multivariable analysis: An introduction. New Haven: Yale University Press; 1996.

7. Talavera JO. Investigación clínica I. Diseños de investigación. Rev Med Inst Mex Seguro Soc. 2011; 49(1):53-8.

8. Rivas-Ruiz R, Castelán-Martínez OD, Pérez M, Talavera JO. Investigación clínica XVII. Prueba chi cuadrada, de lo esperado a lo observado. Rev Med Inst Mex Seguro Soc. 2013;51(5):552-7.

9. Rivas-Ruiz R, Pérez-Rodríguez M, Talavera JO. Investigación clínica XV. Del juicio clínico al modelo estadístico. Diferencia de medias. Prueba t de Student. Rev Med Inst Mex Seguro Soc. 2013;51(3): 300-3.