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Main Authors:	Möllenhoff, Kathrin, Binder, Nadine, Dette, Holger
Format:	Preprint
Published:	2024
Subjects:	Methodology Applications
Online Access:	https://arxiv.org/abs/2401.04490
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author	Möllenhoff, Kathrin Binder, Nadine Dette, Holger
author_facet	Möllenhoff, Kathrin Binder, Nadine Dette, Holger
contents	The identification of similar patient pathways is a crucial task in healthcare analytics. A flexible tool to address this issue are parametric competing risks models, where transition intensities may be specified by a variety of parametric distributions, thus in particular being possibly time-dependent. We assess the similarity between two such models by examining the transitions between different health states. This research introduces a method to measure the maximum differences in transition intensities over time, leading to the development of a test procedure for assessing similarity. We propose a parametric bootstrap approach for this purpose and provide a proof to confirm the validity of this procedure. The performance of our proposed method is evaluated through a simulation study, considering a range of sample sizes, differing amounts of censoring, and various thresholds for similarity. Finally, we demonstrate the practical application of our approach with a case study from urological clinical routine practice, which inspired this research.
format	Preprint
id	arxiv_https___arxiv_org_abs_2401_04490
institution	arXiv
publishDate	2024
record_format	arxiv
spellingShingle	Testing similarity of parametric competing risks models for identifying potentially similar pathways in healthcare Möllenhoff, Kathrin Binder, Nadine Dette, Holger Methodology Applications The identification of similar patient pathways is a crucial task in healthcare analytics. A flexible tool to address this issue are parametric competing risks models, where transition intensities may be specified by a variety of parametric distributions, thus in particular being possibly time-dependent. We assess the similarity between two such models by examining the transitions between different health states. This research introduces a method to measure the maximum differences in transition intensities over time, leading to the development of a test procedure for assessing similarity. We propose a parametric bootstrap approach for this purpose and provide a proof to confirm the validity of this procedure. The performance of our proposed method is evaluated through a simulation study, considering a range of sample sizes, differing amounts of censoring, and various thresholds for similarity. Finally, we demonstrate the practical application of our approach with a case study from urological clinical routine practice, which inspired this research.
title	Testing similarity of parametric competing risks models for identifying potentially similar pathways in healthcare
topic	Methodology Applications
url	https://arxiv.org/abs/2401.04490

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