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An R-Based Landscape Validation of a Competing Risk Model

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Testing Similarity of Parametric Competing Risks Models for Identifying Potentially Similar Pathways in Healthcare.

Kathrin Möllenhoff¹, Nadine Binder², Holger Dette³

¹Institute of Medical Statistics and Computational Biology (IMSB), University of Cologne, Cologne, Germany.

Statistics in Medicine

|October 12, 2024

Summary

This summary is machine-generated.

We developed a new method to compare patient pathways using flexible statistical models. This approach helps identify similar health journeys, improving healthcare analytics and clinical decision-making.

Keywords:

bootstrap multistate models parametric competing risks models routine clinical data similarity small data

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Area of Science:

Healthcare analytics
Biostatistics
Survival analysis

Background:

Identifying similar patient pathways is vital for healthcare analytics.
Parametric competing risks models offer flexibility in analyzing health state transitions.
Assessing similarity between these models requires robust statistical methods.

Purpose of the Study:

To introduce a novel method for measuring the maximum differences in transition intensities over time between two parametric competing risks models.
To develop a statistical test procedure for assessing the similarity of patient pathways.
To validate the proposed method through simulation and a clinical case study.

Main Methods:

Utilizing parametric competing risks models with time-dependent transition intensities.
Developing a method to quantify maximum differences in transition intensities.
Proposing a parametric bootstrap approach for hypothesis testing.
Conducting a simulation study with varying sample sizes and censoring levels.

Main Results:

The proposed method effectively measures differences in transition intensities over time.
The parametric bootstrap procedure is validated for assessing model similarity.
Simulation results demonstrate the method's performance across different scenarios.
The approach is successfully applied to a urological clinical dataset.

Conclusions:

The developed method provides a statistically sound approach to assess similarity between patient pathways modeled by parametric competing risks models.
This tool enhances healthcare analytics by enabling quantitative comparison of health trajectories.
The findings have practical implications for clinical routine, aiding in personalized medicine and treatment strategy evaluation.