Random-effects regression analysis of correlated grouped-time survival data.

Random-E®ects Regression Analysis of Correlated Grouped-Time Survival Data

Spatial Varying Coefficient Regression Model For Relative Risk Factors of Esophageal Cancer Patients

In conventional methods for spatial survival data modeling, it is often assumed that the coefficients of explanatory variables in different regions have a constant effect on survival time. Usually, the spatial correlation of data through a random effect is also included in the model. But in many practical issues, the factors affecting survival time do not have the same effects in different regi...

The Survival Kit: Software to analyze survival data including possibly correlated random effects

The Survival Kit is a Fortran 90 Software intended for survival analysis using proportional hazards models and their extension to frailty models with a single response time. The hazard function is described as the product of a baseline hazard function and a positive (exponential) function of possibly time-dependent fixed and random covariates. Stratified Cox, grouped data and Weibull models can...

Comparison of Random Survival Forests for Competing Risks and Regression Models in Determining Mortality Risk Factors in Breast Cancer Patients in Mahdieh Center, Hamedan, Iran

Introduction: Breast cancer is one of the most common cancers among women worldwide. Patients with cancer may die due to disease progression or other types of events. These different event types are called competing risks. This study aimed to determine the factors affecting the survival of patients with breast cancer using three different approaches: cause-specific hazards regression, subdistri...

A joint model of longitudinal and competing risks survival data with heterogeneous random effects and outlying longitudinal measurements.

This article proposes a joint model for longitudinal measurements and competing risks survival data. The model consists of a linear mixed effects sub-model with t-distributed measurement errors for the longitudinal outcome, a proportional cause-specific hazards frailty sub-model for the survival outcome, and a regression sub-model for the variance-covariance matrix of the multivariate latent ra...