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Classification of disease recurrence using transition likelihoods with expectation-maximization algorithm.

Huijun Jiang1, Quefeng Li1, Jessica T Lin2

  • 1Department of Biostatistics, University of North Carolina, Chapel Hill, North Carolina, USA.

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|July 31, 2022
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Summary
This summary is machine-generated.

Distinguishing infectious disease recurrence causes, such as treatment failure versus reinfection, is vital for control. This study introduces a novel classification method using baseline covariates that accurately differentiates these outcomes, improving upon existing models, especially for small sample sizes.

Keywords:
EM algorithmclassificationinfectious diseasesmalariatransition likelihood

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Infectious Disease Modeling

Background:

  • Infectious disease recurrence can stem from treatment failure or new infections, necessitating accurate differentiation for effective control.
  • Traditional Markov-based multi-state models struggle with unknown disease states, limiting their accuracy in classifying recurrence causes.
  • Distinguishing relapse from reinfection is crucial for understanding disease dynamics and optimizing treatment strategies.

Purpose of the Study:

  • To develop and validate a novel classification method that accurately distinguishes between treatment failure and reinfection in infectious diseases.
  • To demonstrate the utility of baseline covariate transition likelihoods in improving disease state classification.
  • To enhance disease progression models by incorporating additional disease outcomes and improving classification accuracy.

Main Methods:

  • Utilized a multinomial logit model to estimate disease transition probabilities.
  • Incorporated baseline covariate transition information for enhanced classification accuracy.
  • Applied the expectation-maximization (EM) algorithm for parameter estimation, including marginal probabilities of disease outcomes.
  • Conducted a simulation study to compare the proposed classifier with existing two-stage methods.

Main Results:

  • The proposed classifier demonstrated superior accuracy compared to the existing two-stage method, particularly in scenarios with small sample sizes.
  • Baseline covariate transition likelihoods effectively distinguished between different causes of infectious disease recurrence.
  • The method was successfully applied to Plasmodium vivax treatment studies in Cambodia, distinguishing relapse from reinfection.

Conclusions:

  • The developed method offers a more accurate approach to classifying infectious disease recurrence causes than traditional models.
  • This approach improves disease control strategies by providing precise differentiation between treatment failure and reinfection.
  • The findings have practical implications for managing infectious diseases like malaria and Plasmodium vivax, especially in resource-limited settings.