It was possible, using Cox models, to correct for the waiting-time bias, but the strong protective effect of termination of hospitalization on the risk for infection remained difficult to interpret.

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

The Cox regression model with a shared frailty factor allows for unobserved heterogeneity or for statistical dependence between the observed survival times. Estimation in this model when the frailties ...

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.

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 ...

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 ...