Logistic regression is often used instead of Cox regression to analyse genome-wide association studies (GWAS) of single-nucleotide polymorphisms (SNPs) and disease outcomes with cohort and case-cohort ...

We present an approach for correcting for interobserver measurement error in an ordinal logistic regression model taking into account also the variability of the ...

Multicenter Phase I/II Study of Cetuximab With Paclitaxel and Carboplatin in Untreated Patients With Stage IV Non–Small-Cell Lung Cancer Data from 1,066 patients recruited from nine European centers ...

Bankruptcy prediction has traditionally relied on statistical approaches such as Altman’s Z-score, which use financial ratios ...

As the coronavirus disease 2019 (COVID-19) pandemic has spread across the world, vast amounts of bioinformatics data have been created and analyzed, and logistic regression models have been key to ...

A class of conditional logistic regression models for clustered binary data is considered. This includes the polychotomous logistic model of Rosner (1984) as a special case. Properties such as the ...

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 ...