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.

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 ...

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 ...

The Dantzig selector (DS) is a recent approach of estimation in high-dimensional linear regression models with a large number of explanatory variables and a relatively small number of observations. As ...

Learn to apply multiple regression techniques to predict continuous outcomes, use logistic regression for binary outcomes, and employ Cox regression for survival analysis.

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 ...

Outcome was self-reported overuse running-related injury. A multistate Cox regression model was used to estimate adjusted hazard rate ratios (HRR). Results Among 5205 runners (mean age 45.8 years, ...