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 ...

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The first step in conducting a regression-based study is to specify a model. In real applications, this is usually the most challenging step - deciding which variables “belong” in the model and which ...

Journal of the Royal Statistical Society. Series C (Applied Statistics), Vol. 48, No. 3 (1999), pp. 313-329 (17 pages) The number of variables in a regression model is often too large and a more ...

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 ...

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 ...

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 ...

Researchers have found that there is a link between migraine and hypertension among adolescent patients, especially patients with severe migraine.

Katz and King have previously proposed a statistical model for multiparty election data. They argue that ordinary least-squares (OLS) regression is inappropriate when the dependent variable measures ...