Related Experiment Video
Updated: Aug 15, 2025

An R-Based Landscape Validation of a Competing Risk Model
Published on: September 16, 2022
The weighted log-rank tests based on stratified clustered survival data: saddle-point p-values and confidence
1Department of Mathematics, Faculty of Education, Ain-Shams University, Cairo, Egypt.
Abstract:
Clinical studies sometimes provide clustered data with censored failure times. A crucial factor of the randomized design that lessens selection bias is the random allocation rule. Given this, the weighted rank tests' p-values for stratified survival clustered sampling based on the random allocation rule are approximated using the double saddle-point approximation technique. For tests of significance and confidence intervals for the treatment effect, this approximation can be utilized. Through simulation experiments, the accuracy of the saddle-point approximation is examined by comparing saddle-point and normal approximations to the exact underlying permutation distribution.
More Related Videos
Related Concept Videos
The Mantel-Cox Log-Rank Test
Comparing the Survival Analysis of Two or More Groups
Wilcoxon Rank-Sum Test
Survival Curves
The Kaplan-Meier estimator is the most common method for constructing survival curves. This...
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...
Assumptions of Survival Analysis

