Related Experiment Video
Updated: Dec 21, 2025

Establishing a Competing Risk Regression Nomogram Model for Survival Data
Published on: October 23, 2020
Survival analysis with change-points in covariate effects
Chun Yin Lee1, K F Lam1,2
1Department of Statistics and Actuarial Science, The University of Hong Kong, Hong Kong, Hong Kong.
Abstract:
We apply a maximal likelihood ratio test for the presence of multiple change-points in the covariate effects based on the Cox regression model. The covariate effect is assumed to change smoothly at one or more unknown change-points. The number of change-points is inferred by a sequential approach. Confidence intervals for the regression and change-point parameters are constructed by a bootstrap method based on Bernstein polynomials conditionally on the number of change-points. The methods are assessed by simulations and are applied to two datasets.
More Related Videos
10:46A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
Published on: December 9, 2015
06:55Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
Published on: January 8, 2020
Related Concept Videos
Comparing the Survival Analysis of Two or More Groups
Assumptions of Survival Analysis
Introduction To Survival Analysis
The primary goal of survival analysis is to estimate survival time—the time...
Survival Tree
Building a Survival Tree
Constructing a...
Parametric Survival Analysis: Weibull and Exponential Methods
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
Censoring Survival Data