Significance of Multiple logistic regression
Multiple logistic regression is a statistical technique utilized to analyze relationships between several independent variables and a dependent variable, while controlling for confounding factors. It serves various applications, such as identifying predictors of anemia, chronic diseases, and suicidal ideation. This method is crucial in exploring associations in studies across multiple fields, including health and psychology. By determining the influence of different factors, multiple logistic regression aids in understanding complex outcomes and enhancing predictive accuracy in research.
Synonyms: Binary logistic regression, Multivariate logistic regression, Logistic regression analysis.
The below excerpts are indicatory and do represent direct quotations or translations. It is your responsibility to fact check each reference.
The concept of Multiple logistic regression in scientific sources
Multiple logistic regression is a statistical method that analyzes relationships between multiple predictor variables and binary outcomes, helping to identify significant factors associated with various health conditions and behaviors while adjusting for confounding variables.
From: International Journal of Environmental Research and Public Health (MDPI)
(1) Multiple logistic regression is a statistical method used to assess the association between family caregiver status and depressive symptoms, reporting results using odds ratios and confidence intervals.[1] (2) Multiple logistic regression models were used to estimate the adjusted odds ratio with a confidence interval after adjusting for variances such as age and smoking habits.[2] (3) Multiple logistic regression was then performed, keeping in the final model the factors with a significance level lower than or equal to 0.05 or considered important according to apriori biological criteria.[3] (4) It is a statistical method used to model the probability of a binary outcome, and in the study, it was used to identify factors associated with a low AUC/MIC.[4] (5) According to the provided information, "Multiple logistic regression" model is used to predict missed appointment among the caregivers in the study population, considering several variables simultaneously.[5]
From: Sustainability Journal (MDPI)
(1) The text mentions "multiple logistic regression recognition model for mine water inrush source based on cluster analysis", referring to a statistical method used to predict the probability of a binary outcome based on multiple predictor variables, specifically in the context of identifying the source of water entering mines.[6]
From: The Malaysian Journal of Medical Sciences
(1) This is a statistical analysis used to examine the relationship between multiple variables and an outcome, and it was used in the study to determine the factors affecting chronic pituitary dysfunction.[7] (2) Multiple logistic regression is a statistical method used to analyze the relationship between multiple predictor variables and a binary outcome.[8] (3) This is a statistical technique used to analyze the relationship between multiple predictor variables and a binary outcome, providing insights into the factors affecting patient transfer decisions.[9] (4) This is a statistical method used to analyze the relationship between multiple independent variables and a dependent variable. The study uses this method.[10] (5) An analytical method used to assess parameters related to infected CSF, such as CSF protein, CSF glucose, CSF appearance, and CSF Gram staining status.[11]
From: Journal of Public Health in Africa
(1) Multiple logistic regression analysis was used to determine associations between demographic variables and the level of awareness, knowledge, and prevention practices related to Lassa fever.[12] (2) This statistical method was used to analyze the data, specifically to examine the association between physical activity and chronic diseases while adjusting for other variables.[13] (3) This is a statistical technique used to determine the relationship between multiple independent variables and a dependent variable, which was employed to examine the association between age, sex, and COVID-19 outcome.[14] (4) Multiple logistic regression model was used to determine the factors found to be significantly associated with high breast disorder detection at recruitment, including age, education, and history of benign breast disease.[15] (5) Multiple logistic regressions were used to analyze the data collected, particularly because the dependent variable was categorical in scale, and the final results helped determine the variables most related based on the largest OR value.[16]
From: African Journal of Primary Health Care and Family Medicine
(1) It is a statistical method used to predict the probability of a binary outcome based on multiple predictor variables.[17] (2) This is a statistical method used to test for significant associations between predictor factors and the uptake of post-partum family planning methods because the outcome data collected were categorical or binary.[18] (3) This is the statistical method used to analyze the data, examining the factors associated with having had an eye examination within the context of the study conducted in the Ashanti region of Ghana.[19] (4) This is a statistical analysis technique used to investigate the association between the outcome variable and selected demographic variables.[20] (5) This is a statistical method used to examine the relationship between multiple independent variables and a dependent variable, such as the belief in chloroquine.[21]
From: South African Family Practice
(1) This analysis was used to investigate the association between pain and the demographic variables of sex and age.[22] (2) Multiple logistic regression analysis was employed to identify factors associated with suicidal ideation, allowing for the assessment of the effect of independent variables while adjusting for probable confounding variables.[23] (3) This is a statistical method used to analyze the relationships between various factors and the practice intentions and preferences of clinical associate students, as applied in the study.[24]
From: Asian Journal of Pharmaceutics
(1) A statistical test used to examine the relationship between multiple independent variables and a categorical dependent variable.[25] (2) Multivariate analysis using this method was performed to obtain adjusted odds ratio.[26] (3) This was performed to assess the influences each factor cytokine and growth factor, and the odds ratio (OR) were calculated.[27]
From: South African Journal of HIV Medicine
(1) This is a statistical technique used to identify factors that are associated with a specific outcome, like virological failure, allowing for a comprehensive understanding.[28] (2) This is a statistical technique used to analyze the relationship between several independent variables and a single dependent variable, while controlling for other factors.[29]
From: South African Journal of Physiotherapy
(1) This is a statistical analysis method that was employed to analyze the relationship between several predictor variables and the likelihood of injury, as a part of the study.[30]
From: Onderstepoort Journal of Veterinary Research
(1) This is a statistical model used to estimate the association of year and system with the odds of culturing an organism.[31]
From: International Journal of Pharmacology
(1) Multiple logistic regression is a statistical method used to predict a binary outcome based on multiple predictor variables, while accounting for potential confounding factors.[32]
From: South African Journal of Psychiatry
(1) The significant models for patients who relapsed included poor adherence due to side-effects, poor adherence due to lack of insight, and co-morbid depressed mood, as the text indicates.[33] (2) The result of these analyses showed that significant variables of depressive features were job type, physical activity, taking dietary supplements, interacting with neighbours, relationship with spouses, hobbies and ADL.[34] (3) This is a statistical method used in the study to determine the true associations between socio-demographic variables and various forms of child abuse, after correcting for other variables.[35] (4) A statistical analysis method used to examine the relationships between several factors and the likelihood of a certain outcome, and this method was used to analyze the data.[36] (5) This statistical model was employed to assess which factors were independently associated with autism spectrum disorder symptoms, providing a deeper understanding of their influence.[37]