2014
DOI: 10.11613/bm.2014.003
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Understanding logistic regression analysis

Abstract: Logistic regression is used to obtain odds ratio in the presence of more than one explanatory variable. The procedure is quite similar to multiple linear regression, with the exception that the response variable is binomial. The result is the impact of each variable on the odds ratio of the observed event of interest. The main advantage is to avoid confounding effects by analyzing the association of all variables together. In this article, we explain the logistic regression procedure using examples to make it … Show more

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Cited by 872 publications
(588 citation statements)
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“…Consequently, the specific model that was used was a binary logistic model due to the fact that the dependent variable was binomial (Khan & Raeside, 1997;Sperandei, 2014;Khan & Rahman, 2016). The outcome of interest, matric completion, was constructed using the information on the highest level of education attained.…”
Section: Resultsmentioning
confidence: 99%
“…Consequently, the specific model that was used was a binary logistic model due to the fact that the dependent variable was binomial (Khan & Raeside, 1997;Sperandei, 2014;Khan & Rahman, 2016). The outcome of interest, matric completion, was constructed using the information on the highest level of education attained.…”
Section: Resultsmentioning
confidence: 99%
“…As is common when building a logistic regression model, we used a less stringent threshold for significance when deciding which variables to enter into the multivariate model, since the true size and significance of the effect of certain variables may be masked by potential confounding variables, and revealed after adjusting for those variables in the model. 23 No inferences are drawn from these univariate analyses; they are simply a screening to determine which independent variables are included in the model. Any variable with an adjusted odds ratio with a 95% confidence interval that did not cross 1 in the multivariate model was considered statistically significant.…”
Section: Methodsmentioning
confidence: 99%
“…As the significance of variables may change when considered in relation to other variables, any potential predictor variable with a P value of less than or equal to .25 was included in the initial regression model. 41 Potential predictors were baseline and short-term outcome variables and duration (in months) to long-term follow-up. Variables that were not considered significant were removed one by one, starting with the least significant variable, until a final model was achieved.…”
Section: Logistic Regressionmentioning
confidence: 99%