Estimating Risk Factors for Pathogenic Dose Accrual From Longitudinal Data

Daniel K Sewell; Kelly K Baker

doi:10.1002/sim.70291

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Estimating Risk Factors for Pathogenic Dose Accrual From Longitudinal Data

Journal article

Open access

Peer reviewed

Estimating Risk Factors for Pathogenic Dose Accrual From Longitudinal Data

Daniel K Sewell and Kelly K Baker

Statistics in medicine, Vol.44(23-24), e70291

10/2025

DOI: 10.1002/sim.70291

PMCID: PMC12503088

PMID: 41055559

Appears in UI Libraries Support Open Access

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Abstract

Estimating risk factors for the incidence of a disease is crucial for understanding its etiology. For diseases caused by enteric pathogens, off-the-shelf statistical model-based approaches do not consider the biological mechanisms through which infection occurs and thus can only be used to make comparatively weak statements about the association between risk factors and incidence. Building off of established work in quantitative microbiological risk assessment, we propose a new approach to determining the association between risk factors and dose accrual rates. Our more mechanistic approach achieves a higher degree of biological plausibility, incorporates currently ignored sources of variability, and provides regression parameters that are easily interpretable as the dose accrual rate ratio due to changes in the risk factors under study. We also describe a method for leveraging information across multiple pathogens. The proposed methods are available as an R package at https://github.com/dksewell/dare. Our simulation study shows unacceptable coverage rates from generalized linear models, while the proposed approach empirically maintains the nominal rate even when the model is misspecified. Finally, we demonstrated our proposed approach by applying our method to infant data obtained through the PATHOME study (https://reporter.nih.gov/project-details/10227256), discovering the impact of various environmental factors on infant enteric infections.

Computer Simulation

Humans

Incidence

Infant

Linear Models

Longitudinal Studies

Models, Statistical

Risk Assessment - methods

Risk Factors

UIOWA OA Agreement

Details

Title: Subtitle: Estimating Risk Factors for Pathogenic Dose Accrual From Longitudinal Data
Creators: Daniel K Sewell - University of Iowa
Kelly K Baker - University at Buffalo, State University of New York
Resource Type: Journal article
Publication Details: Statistics in medicine, Vol.44(23-24), e70291
DOI: 10.1002/sim.70291
PMID: 41055559
PMCID: PMC12503088
NLM abbreviation: Stat Med
ISSN: 1097-0258
eISSN: 1097-0258
Publisher: Wiley
Grant note: R01 TW011795 / FIC NIH HHS
Language: English
Date published: 10/2025
Academic Unit: Occupational and Environmental Health; Epidemiology; Biostatistics
Record Identifier: 9984969113802771

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