Similarity of competing risks models with constant intensities in an application to clinical healthcare pathways involving prostate cancer surgery

TL;DR

This paper introduces a new statistical approach to identify similar healthcare pathways in clinical data by comparing competing risks models with constant intensities, aiding in understanding patient trajectories.

Contribution

It proposes a novel bootstrap testing procedure for pathway similarity based on multi-state competing risks models with constant transition intensities.

Findings

The method effectively distinguishes similar and dissimilar pathways in simulations.

Application to urological clinical data demonstrates practical utility.

Performance varies with sample size and similarity thresholds.

Abstract

The recent availability of routine medical data, especially in a university-clinical context, may enable the discovery of typical healthcare pathways, i.e., typical temporal sequences of clinical interventions or hospital readmissions. However, such pathways are heterogeneous in a large provider such as a university hospital, and it is important to identify similar care pathways that can still be considered typical pathways. We understand the pathway as a temporal process with possible transitions from a single initial treatment state to hospital readmission of different types, which constitutes a competing risk setting. In this paper, we propose a multi-state model-based approach to uncover pathway similarity between two groups of individuals. We describe a new bootstrap procedure for testing the similarity of transition intensities from two competing risk models with constant transition intensities. In a large simulation study, we investigate the performance of our similarity approach with respect to different sample sizes and different similarity thresholds. The studies are motivated by an application from urological clinical routine and we show how the results can be transferred to the application example.