Primer on binary logistic regression

Harris, Jenine K.

doi:10.1136/fmch-2021-001290

Cited by 60 publications

(40 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…This study used binary logistic regressions for the primary analyses, which is appropriate for analyzing survey data in cross-sectional research designs [ 36 ]. However, the odds ratio measures can overestimate the prevalence ratio in some cases [ 37 ].…”

Section: Discussionmentioning

confidence: 99%

Coordination and Perceived Support for Return to Work: A Cross-Sectional Study among Patients in Swedish Healthcare

Berglund

Friberg

Engblom

et al. 2022

IJERPH

View full text Add to dashboard Cite

Background: Receiving support from a return-to-work (RTW) coordinator (RTWC) may be beneficial for people on long-term sick leave. The aim of this study was to investigate whether the number of contacts with an RTWC and their involvement in designing rehabilitation plans for the patients were associated with perceiving support for RTW, emotional response to the RTWC, and healthcare utilization. Methods: In this cross-sectional study, 274 patients who had recently been in contact with an RTWC in Swedish primary or psychiatric care answered questions regarding their interaction with an RTWC, perceived support for RTW, and emotional response to the RTWC. Results: Having more contact with an RTWC was associated with perceiving more support in the RTW process (adjusted OR 4.14, 95% CI 1.49–11.47). RTWC involvement in designing a rehabilitation plan for the patient was associated with perceiving more support in the RTW process from an RTWC and having a more positive emotional response to the RTWC. Conclusions: From the patient’s perspective, this study indicates that the involvement of an RTWC and receiving a rehabilitation plan that an RTWC has helped to design might be perceived as important in the RTW process.

show abstract

Section: Discussionmentioning

confidence: 99%

Coordination and Perceived Support for Return to Work: A Cross-Sectional Study among Patients in Swedish Healthcare

Berglund

Friberg

Engblom

et al. 2022

IJERPH

View full text Add to dashboard Cite

show abstract

“…Binary logistic regression relies upon four assumptions: (1) the dependent variable must be dichotomous; (2) “the observations must be independent; ” (3) “there must be no perfect multicollinearity among independent variables; ” (4) and “the continuous predictors … [must be] linearly related to a transformed version of the outcome (linearity)” (Harris, 2021, p. 3). All of the aforementioned conditions were met.…”

Section: Methodsmentioning

confidence: 99%

Predictors of public support for police accreditation

Abner

2022

PIJPSM

View full text Add to dashboard Cite

PurposeThis study aims to identify the predictors of public support for police accreditation under four different scenarios that vary based upon whether or not accreditation requires a property tax increase and whether or not local police support accreditation.Design/methodology/approachThis study draws upon survey data from 998 US adults. Four binary logistic models are employed to assess support for accreditation under four different scenarios.FindingsIndividual-level factors, including age, education, race, region, generalized trust, political ideology, and volunteer experience, are important predictors of support for police accreditation depending on the scenario presented. Community-level factors including perceptions of police performance and perceptions of property taxes are also important predictors of support for police accreditation depending on the scenario presented. Support for police accreditation is highest when police accreditation does not require a property tax increase and when local police support accreditation. Sixty-six percent of respondents support police accreditation under this scenario.Originality/valueThis article represents the first published study on the predictors of support for police accreditation. The findings from this study have important implications for implementing police reform given recent calls by activists to both reform the police and to defund them.

show abstract

“…Logistic regression is a well-known and widely used technique for predicting binary variables and carrying out discriminant analysis when the predictor variables are not all normally distributed [ 14 ]. It was used for classification here by choosing the predicted group as the group with the larger predicted probability of membership.…”

Section: Methodsmentioning

confidence: 99%

The Use of Machine Learning to Reduce Overtreatment of the Axilla in Breast Cancer: Retrospective Cohort Study

et al. 2022

View full text Add to dashboard Cite

Background Patients with early breast cancer undergoing primary surgery, who have low axillary nodal burden, can safely forego axillary node clearance (ANC). However, routine use of axillary ultrasound (AUS) leads to 43% of patients in this group having ANC unnecessarily, following a positive AUS. The intersection of machine learning with medicine can provide innovative ways to understand specific risks within large patient data sets, but this has not yet been trialed in the arena of axillary node management in breast cancer. Objective The objective of this study was to assess if machine learning techniques could be used to improve preoperative identification of patients with low and high axillary metastatic burden. Methods A single-center retrospective analysis was performed on patients with breast cancer who had a preoperative AUS, and the specificity and sensitivity of AUS were calculated. Standard statistical methods and machine learning methods, including artificial neural network, naive Bayes, support vector machine, and random forest, were applied to the data to see if they could improve the accuracy of preoperative AUS to better discern high and low axillary burden. Results The study included 459 patients; 142 (31%) had a positive AUS; among this group, 88 (62%) had 2 or fewer macrometastatic nodes at ANC. Logistic regression outperformed AUS (specificity 0.950 vs 0.809). Of all the methods, the artificial neural network had the highest accuracy (0.919). Interestingly, AUS had the highest sensitivity of all methods (0.777), underlining its utility in this setting. Conclusions We demonstrated that machine learning improves identification of the important subgroup of patients with no palpable axillary disease, positive ultrasound, and more than 2 metastatically involved nodes. A negative ultrasound in patients with no palpable lymphadenopathy is highly indicative of low axillary burden, and it is unclear whether sentinel node biopsy adds value in this situation. Further studies with larger patient numbers focusing on specific breast cancer subgroups are required to refine these techniques in this setting.

show abstract

Primer on binary logistic regression

Cited by 60 publications

References 21 publications

Coordination and Perceived Support for Return to Work: A Cross-Sectional Study among Patients in Swedish Healthcare

Coordination and Perceived Support for Return to Work: A Cross-Sectional Study among Patients in Swedish Healthcare

Predictors of public support for police accreditation

The Use of Machine Learning to Reduce Overtreatment of the Axilla in Breast Cancer: Retrospective Cohort Study

Contact Info

Product

Resources

About