Recurrent events modeling based on a reflected Brownian motion with application to hypoglycemia

Yingfa Xie; Haoda Fu; Yuan Huang; Vladimir Pozdnyakov; Jun Yan

doi:10.1093/biostatistics/kxae053

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Recurrent events modeling based on a reflected Brownian motion with application to hypoglycemia

Journal article

Peer reviewed

Recurrent events modeling based on a reflected Brownian motion with application to hypoglycemia

Yingfa Xie, Haoda Fu, Yuan Huang, Vladimir Pozdnyakov and Jun Yan

Biostatistics (Oxford, England), Vol.26(1), kxae053

12/31/2024

DOI: 10.1093/biostatistics/kxae053

PMID: 39865700

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Abstract

Patients with type 2 diabetes need to closely monitor blood sugar levels as their routine diabetes self-management. Although many treatment agents aim to tightly control blood sugar, hypoglycemia often stands as an adverse event. In practice, patients can observe hypoglycemic events more easily than hyperglycemic events due to the perception of neurogenic symptoms. We propose to model each patient's observed hypoglycemic event as a lower boundary crossing event for a reflected Brownian motion with an upper reflection barrier. The lower boundary is set by clinical standards. To capture patient heterogeneity and within-patient dependence, covariates and a patient level frailty are incorporated into the volatility and the upper reflection barrier. This framework provides quantification for the underlying glucose level variability, patients heterogeneity, and risk factors' impact on glucose. We make inferences based on a Bayesian framework using Markov chain Monte Carlo. Two model comparison criteria, the deviance information criterion and the logarithm of the pseudo-marginal likelihood, are used for model selection. The methodology is validated in simulation studies. In analyzing a dataset from the diabetic patients in the DURABLE trial, our model provides adequate fit, generates data similar to the observed data, and offers insights that could be missed by other models.

Bayes Theorem

Blood Glucose - metabolism

Diabetes Mellitus, Type 2 - blood

Diabetes Mellitus, Type 2 - complications

Humans

Hypoglycemia - blood

Hypoglycemic Agents - therapeutic use

Markov Chains

Models, Statistical

Monte Carlo Method

Details

Title: Subtitle: Recurrent events modeling based on a reflected Brownian motion with application to hypoglycemia
Creators: Yingfa Xie - University of Connecticut
Haoda Fu - Eli Lilly
Yuan Huang - Yale University
Vladimir Pozdnyakov - University of Connecticut
Jun Yan - University of Connecticut
Resource Type: Journal article
Publication Details: Biostatistics (Oxford, England), Vol.26(1), kxae053
Publisher: OXFORD UNIV PRESS
DOI: 10.1093/biostatistics/kxae053
PMID: 39865700
ISSN: 1468-4357
eISSN: 1468-4357
Language: English
Date published: 12/31/2024
Academic Unit: Biostatistics
Record Identifier: 9984781373702771

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