Proper handling of continuous variables is crucial in healthcare research, for example, within regression modelling for descriptive, explanatory, or predictive purposes. However, inadequate methods ...

Logistic Regression is a widely used model in Machine Learning. It is used in binary classification, where output variable can only take binary values. Some real world examples where Logistic ...

This article will cover the basic theory behind logistic regression, the types of logistic regression, when to use them and take you through a worked example.

Different types of multivariable analysis: generalized linear models Generalized linear models (GLMs, table 2) are a flexible and powerful class of statistical models widely used in multivariable ...

Objective To develop a multivariable model for predicting the progression of systemic sclerosis-associated interstitial lung disease (SSc-ILD) over 52 weeks.Methods We used logistic regression models ...

The predictive performance of each candidate prognostic factor of chronic pain was estimated using univariable logistic regression analysis. These analyses were not used to decide which prognostic ...

Methods: The national inpatient sample database from 2016-2019 was queried to identify patients with a primary diagnosis of stroke and stratified based on the presence of MM as a secondary diagnosis.

3.4 Multivariate logistic regression analysis of risk factors related to ovarian cancer CA19-9, CA125, NLR, PLR, BDNF, lymph node metastasis and the CV were all risk factors for ovarian cancer.