Although Cox and logistic regression models have been compared previously in cohort studies, this work does not completely cover the GWAS setting nor extend to the case-cohort study design.

I predict you'll find this logistic regression example with R to be helpful for gleaning useful information from common binary classification problems.

Linear and logistic regression models are essential tools for quantifying the relationship between outcomes and exposures. Understanding the mathematics behind these models and being able to apply ...

Logistic regression enables you to investigate the relationship between a categorical outcome and a set of explanatory variables. The outcome, or response, can be dichotomous (yes, no) or ordinal (low ...

Mixed Effects Logistic Regression Models for Multiple Longitudinal Binary Functional Limitation Responses with Informative Drop-Out and Confounding by Baseline Outcomes Thomas R. Ten Have, Beth A.

The log-logistic distribution has a non-monotonic hazard function which makes it suitable for modelling some sets of cancer survival data. A log-logistic regression model is described in which the ...

In these kinds of situations, we would prefer a model that is easy to interpret, such as the logistic regression model. The Delta-p statistics makes the interpretation of the coefficients even easier.

Logistic regression is a statistical method used to examine the relationship between a binary outcome variable and one or more explanatory variables. It is a special case of a regression model that ...