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

Given logistic regression is substantially more computationally efficient than Cox regression in both settings, we propose a two-step approach to GWAS in cohort and case-cohort studies.

Pantelis Samartsidis, Claudia R. Eickhoff, Simon B. Eickhoff, Tor D. Wager, Lisa Feldman Barrett, Shir Atzil, Timothy D. Johnson, Thomas E. Nichols, Bayesian log-Gaussian Cox process regression, ...

The functional linear Cox regression model incorporates a functional principal component analysis for modeling the functional predictors and a high-dimensional Cox regression model to characterize the ...

Overall survival (OS) was similar in all three studies. 819 pts were enrolled in 1998–2004 and 798 pts of them were evaluable for this analysis: 85% of pts had stage IV disease and PS=1. Univariate ...

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 ...