In epidemiological studies, continuous covariates often are measured with error and categorical covariates often are misclassified. Using the logistic regression ...

We consider estimation of mixed-effects logistic regression models for longitudinal data when missing outcomes are not missing at random. A typology of missingness mechanisms is presented that ...

If you are a researcher or student with experience in multiple linear regression and want to learn about logistic regression, Logistic Regression Using the SAS System: Theory and Application is for ...

Logistic regression is a powerful statistical method that is used to model the probability that a set of explanatory (independent or predictor) variables predict data in an outcome (dependent or ...

Modern statistical modelling is increasingly focused on reducing bias and enhancing the accuracy of parameter estimates. Traditional maximum likelihood estimation, while powerful, can encounter ...

A machine learning model using basic clinical data can predict PH risk, identifying key predictors like low hemoglobin and elevated NT-proBNP. Researchers have developed a machine learning model that ...