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Updated: Jul 7, 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

A risk-adjusted CUSUM in continuous time based on the Cox model.

Pinaki Biswas¹, John D Kalbfleisch

¹Global Medical Research and Development, Pfizer Inc., New York, NY 10017, USA. pinaki.biswas@pfizer.com

Statistics in Medicine

|February 22, 2008

Summary

This study introduces a novel risk-adjusted cumulative summation (CUSUM) method for monitoring organ transplant facility outcomes. The new CUSUM procedure helps detect upward trends in failure rates, improving patient safety and care quality.

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Establishing a Competing Risk Regression Nomogram Model for Survival Data

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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
Medical Informatics
Public Health

Background:

Monitoring healthcare facility outcomes is crucial for patient safety and quality improvement.
The cumulative summation (CUSUM) technique is a proven sequential monitoring tool in manufacturing and process control.
CUSUM has recently been proposed for medical applications, including monitoring clinical outcomes.

Purpose of the Study:

To introduce a risk-adjusted cumulative summation (CUSUM) procedure for monitoring failure time outcomes in clinical practice.
To adapt the CUSUM technique for assessing organ transplant facility performance.
To evaluate the performance of the proposed risk-adjusted CUSUM scheme.

Main Methods:

Development of a risk-adjusted CUSUM procedure utilizing the Cox proportional hazards model for failure time data.
Derivation of theoretical approximations for the average run length (ARL) of the proposed CUSUM scheme.
Evaluation of the proposed CUSUM procedure and approximations through simulation studies.

Main Results:

The proposed risk-adjusted CUSUM procedure effectively monitors failure time outcomes in a risk-adjusted manner.
Theoretical approximations for average run length provide insights into the performance of the CUSUM scheme.
Simulations demonstrate the utility and accuracy of the proposed method.

Conclusions:

The risk-adjusted CUSUM procedure offers a valuable tool for monitoring organ transplant facility performance.
This method enables early detection of adverse trends in failure rates, facilitating timely interventions.
The proposed approach enhances the application of CUSUM techniques in medical outcome monitoring.