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Estimating Risk Factors for Pathogenic Dose Accrual From Longitudinal Data.

Daniel K Sewell1, Kelly K Baker2,3

  • 1Department of Biostatistics, University of Iowa, Iowa City, Iowa, USA.

Statistics in Medicine
|October 7, 2025
PubMed
Summary

A new mechanistic approach improves understanding of disease risk factors by analyzing dose accrual rates. This method offers more biologically plausible results than traditional models, aiding in the study of enteric pathogen infections.

Keywords:
dose–responseincidenceinfectious diseasequantitative microbial risk assessment

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Area of Science:

  • Microbiology
  • Epidemiology
  • Biostatistics

Background:

  • Estimating disease risk factors is vital for understanding etiology.
  • Current statistical models for enteric pathogen diseases lack biological mechanism consideration, limiting association statements.
  • Quantitative microbiological risk assessment provides a foundation for mechanistic modeling.

Purpose of the Study:

  • To propose a novel, mechanistic approach for determining the association between risk factors and dose accrual rates for enteric pathogens.
  • To develop regression parameters interpretable as dose accrual rate ratios.
  • To provide a method for leveraging information across multiple pathogens.

Main Methods:

  • Developed a mechanistic approach building on quantitative microbiological risk assessment.
  • Incorporated previously ignored sources of variability.
  • Leveraged information across multiple pathogens.
  • Created an R package (dare) for the proposed methods.

Main Results:

  • The proposed approach demonstrates higher biological plausibility and interpretability compared to traditional methods.
  • Simulation studies showed generalized linear models had unacceptable coverage rates, while the proposed approach maintained nominal rates even when misspecified.
  • Application to infant data from the PATHOME study identified environmental factors impacting enteric infections.

Conclusions:

  • The proposed mechanistic approach offers a more robust and biologically plausible method for analyzing disease risk factors and dose accrual rates.
  • This method improves upon existing statistical models, particularly for enteric pathogen-related diseases.
  • The approach is validated through simulations and real-world application in infant enteric infection studies.