Assessment of the factors that affect fast-track or early extubation following pediatric cardiac surgery: Logistic regression in clinical studies

In recent years, logistic regression, which is an extension of linear regression, is often used to investigate the independent effect of a variable on the binomial outcome (dependent or response) in medical research. The fact that it allows the use of both continuous and categorical variables allows logistic regression to be preferred. It also provides the ability to adjust for multiple predictors (independent or explanatory) and this makes logistic regression particularly useful for analysis of observational data, when adjustment is needed to reduce the potential bias resulting from differences in the groups compared. [1] The use of standard linear regression for a two-level outcome in scientific research leaves researchers with very unsatisfactory results. When linear regression is used, the outcome variability is the same for all values of the predictors. However, when the outcome variable has two categories, the assumption of constant variability does not work and, in this case, linear regression is insufficient. The logistic regression developed to fill this gap assumes a relationship between the predictor variable and the logit of the outcome. According to the outcome variable and the structure of the predictor, the researcher should decide which type of regression to use. For instance, when the predictor variable is continuous or categorical, different types of regression are used depending on the nature of the outcome variable. If the outcome variable is continuous, linear regression is used, but it is not easy to encounter a continuous outcome in practice. To illustrate, a researcher who is willing to find out the odds of having cardiovascular disease in a patient with a smoking habit is faced with a binary outcome, such as cardiovascular disease (present/ absent). At this time, it is not possible to use linear regression, as linearity, normality and continuity assumptions of linear regression are violated when the outcome variable is not continuous. This need, which is the starting point of logistic regression, is defined in different ways as binary, multinomial or ordinal, depending on the type of outcome variable. Binary logistic regression is used, when the outcome variable has two outcomes, such as (present/absent) or (dead/alive). However, the outcome variable (drug X, drug Y, drug Z) may have more than two categories. There is no order of importance for drugs in themselves and, in this case, multinomial logistic regression is preferred. However, if the outcome variable is ordered such as cancer stages (Stage I-II-III-IV), then ordinal logistic regression should be used. Examples of logistic regression are quite common in the literature. [2,3] As in all models, certain assumptions are made to fit the model to the data. Logistic regression does not assume a linear relationship between the dependent and independent variables, but between the logit of the outcome and the predictor values. [4] The outcome variable must be categorical; the predictor variables need not be interval; nor normally distributed, nor linearly related, nor of equal variance within each group, and finally, the categories (groups) must be mutually