Examples of 'multiple logistic regression' in a sentence
Meaning of "multiple logistic regression"
Multiple logistic regression is a statistical analysis technique used to predict or explain the relationship between multiple independent variables and a binary or categorical dependent variable. It is commonly employed in various fields, such as social sciences, epidemiology, and medical research
How to use "multiple logistic regression" in a sentence
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multiple logistic regression
The multiple logistic regression model was used.
Confounder control was performed by multiple logistic regression.
Multiple logistic regression analyses were carried out.
Age remains significant in multiple logistic regression analyses.
Multiple logistic regression was used for multivariate analysis.
Statistical analysis included multiple logistic regression models.
Multiple logistic regression models were built to investigate these associations.
The probability of hypertension was assessed by multiple logistic regression.
Multiple logistic regression was adjusted for age and sex.
Multivariate analysis using multiple logistic regression was employed to study.
Multiple logistic regression analyses controlled for potentially confounding factors.
All variables were then tested in a multiple logistic regression analysis.
Multiple logistic regression analysis identified independent risk factors and confounders.
The statistical model used was the multiple logistic regression.
Multiple logistic regression was used to exclude the influence of possible confounding variables.
See also
This bias can be partially controlled by multiple logistic regression data analysis.
Univariate and multiple logistic regression analyses were used to determine predictors of depression.
The adjustment of possible confounding variables was performed using multiple logistic regression.
Multiple logistic regression was used to obtain odds ratios for pregnancy outcomes.
Significant variables by the univariate logistic regression were analyzed by the multiple logistic regression.
Multiple logistic regression was also applied to analyze the clinical features and pathology.
Variables significantly related to nutritional status were used in the multiple logistic regression model.
Multiple logistic regression was applied to find out the factors associated with smoking.
The stepwise forward method was used to construct the multiple logistic regression model.
A bivariate analysis and a multiple logistic regression were executed to identify potential risk factors.
The impact of age on adverse pregnancy outcomes was assessed using multiple logistic regression.
Multiple logistic regression was used to assess relationships between being vaccinated and selected characteristics.
These parameters are used to design a final index using a multiple logistic regression approach.
Multiple logistic regression was applied to evaluate the factors that could influence return to work.
These were therefore included in the multiple logistic regression model with retrograde variable elimination.
Multiple logistic regression analysis of age and body weight was significant for failure of wean.
Risk factors for low birth weight according to the multiple logistic regression model.
Univariate analyzes and multiple logistic regression were performed to verify association between the variables.
In addition to descriptive analyses, the study included multiple logistic regression.
Multiple logistic regression analysis was used to identify the sociodemographic characteristics associated with the outcomes.
ORs estimated by multiple logistic regression.
The multiple logistic regression model was used to identify independent variables associated with the outcome.
Table 3 shows the multiple logistic regression models.
Multiple logistic regression multivariate analysis was conducted with MACE as a dependent variable.
Then, we carried out the hierarchical multiple logistic regression analysis.
Multiple logistic regression was used to test independent correlates for the presence of CAD.
To do this, correlation analyzes were performed, and multiple logistic regression analyzes.
The multiple logistic regression analysis was used to calculate the adjusted odds ratios OR.
Depending on the outcome, analysis of covariance or multiple logistic regression is used.
Multiple logistic regression was used to identify the characteristics associated with HRT use.
Table 3 shows the result of the multiple logistic regression model adjustment.
A multiple logistic regression model was used to model relationships between selected characteristics and OC use.
Table 4 shows the p values obtained upon multiple logistic regression analysis.
The multiple logistic regression model is given by,.
Table 3 presents the results from the multiple logistic regression analysis.
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