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 derive information bounds for the regression parameters in Cox models when data are missing at random. These calculations are of interest for understanding the behavior of efficient estimation in ...

Dublin, Sept. 02, 2024 (GLOBE NEWSWIRE) -- The "Multiple Linear Regression, Logistic Regression, and Survival Analysis" webinar has been added to ResearchAndMarkets.com's offering. In this ...

In this article, we extend the information matrix tests proposed by White (1982) for detecting parametric model misspecification to the partial likelihood setting with particular interest in the Cox ...

Results show that the optimal design had the highest power and accurate effect size estimation under the Cox regression model. Surprisingly, logistic regression achieved similar power with much lower ...

Lasso-Cox analysis uses the “glmnet” R software package to integrate survival time, survival state, and gene expression data to screen and identify candidate ARGs for constructing prognostic models to ...