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Related Experiment Video

Updated: Jul 2, 2026

An R-Based Landscape Validation of a Competing Risk Model

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

On assessing surrogacy in a single trial setting using a semicompeting risks paradigm.

Debashis Ghosh¹

¹Departments of Statistics and Public Health Sciences, The Pennsylvania State University, University Park, Pennsylvania 16802, USA. ghoshd@psu.edu

|September 2, 2008

Summary

This study introduces a new statistical framework for surrogate endpoints in clinical trials, treating them as semicompeting risks. This approach offers novel methods for analyzing biomarker data and their association with true endpoints.

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Establishing a Competing Risk Regression Nomogram Model for Survival Data

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Last Updated: Jul 2, 2026

An R-Based Landscape Validation of a Competing Risk Model

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Published on: September 16, 2022

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

Area of Science:

Biostatistics
Clinical Trial Design
Biomarker Research

Background:

Growing interest in biomarkers as surrogate endpoints in clinical trials.
Need for robust statistical methods to evaluate surrogacy.

Purpose of the Study:

To propose a novel statistical framework for surrogate endpoints using semicompeting risks data.
To develop new estimation and inferential procedures for measures of surrogacy.

Main Methods:

Viewing surrogate and true endpoints as semicompeting risks data.
Developing asymmetrical treatment for surrogate and true endpoints.
Proposing novel estimation and inferential procedures for relative effect and adjusted association.

Main Results:

Demonstrated the applicability of the semicompeting risks approach to surrogate endpoint analysis.
Illustrated novel methodologies with simulated data and a leukemia clinical trial dataset.

Conclusions:

The semicompeting risks framework provides a new perspective on surrogate endpoint evaluation.
The proposed methods offer advancements in analyzing biomarker data in single clinical trials.