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Related Experiment Video

Updated: Jul 12, 2025

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

Win-loss parameters for right-censored event data, with application to recurrent events.

Erik T Parner¹, Morten Overgaard¹

¹Section for Biostatistics, Department of Public Health, Aarhus University, Aarhus, Denmark.

Statistics in Medicine

|October 28, 2023

Summary

This study introduces a new statistical method for analyzing clinical trial data, comparing death and recurrent events like hospitalizations. The proposed win ratio inference method accurately handles censored data, even in small sample sizes.

Keywords:

IPCW Win ratio censoring inverse-probability-of-censoring weighting nonparametrics prioritized events

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Last Updated: Jul 12, 2025

Establishing a Competing Risk Regression Nomogram Model for Survival Data

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Published on: September 16, 2022

Area of Science:

Biostatistics
Clinical Trials
Survival Analysis

Background:

The win ratio is a popular method for comparing multiple, prioritized events in clinical cohort studies.
Existing literature lacks robust inference methods for win ratios with censored event data, especially for recurrent events.
Recurrent events, such as hospitalizations, are common in clinical studies and require specialized analysis.

Purpose of the Study:

To propose and evaluate a novel statistical inference method for win-loss parameters, specifically for death and recurrent event outcomes.
To address the gap in analyzing prioritized, censored, and recurrent event data in clinical research.
To provide a statistically sound approach for win ratio calculation in the presence of censoring and recurrent events.

Main Methods:

Development of statistical inference for win-loss parameters considering death and a recurrent event.
Application of the method under independent right-censoring assumptions.
Simulation studies to assess the small sample properties and accuracy of the proposed variance formula.

Main Results:

The proposed inference method demonstrates accurate variance estimation, even with small sample sizes in simulations.
The method is successfully applied to a real-world dataset from a randomized clinical trial.
The study validates the utility of the new method for analyzing complex clinical event data.

Conclusions:

The developed statistical inference method provides a reliable tool for analyzing death and recurrent events in clinical trials with censored data.
The findings contribute to the advancement of statistical methodologies for win ratio analysis in biostatistics.
The method offers a practical solution for researchers dealing with prioritized, recurrent, and censored outcomes.