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

In this article we propose a class of estimation functions for the vector of regression parameters in the Cox proportional hazards model with possibly time-dependent covariates by incorporating the ...

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 ...

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.

Background The specific relationship between different blood pressure measures and the risk of aortic aneurysms (AA) remains ...

Thus, the PROC MI procedure may not be well suited for carrying out Cox regression with missing covariate data or a longitudinal analysis with missing discrete outcomes and/or covariates, for example.

Methods: Pts with advanced, well-differentiated pancreatic endocrine tumors and disease progression in the past 12 months were randomized (1:1) to SU 37.5 mg daily or placebo, each with best ...