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A Capture-Recapture-based Ascertainment Probability Weighting Method for Effect Estimation With Under-ascertained

Carl Bonander1,2, Anton Nilsson3, Huiqi Li1

  • 1From the School of Public Health and Community Medicine, Institute of Medicine, University of Gothenburg, Gothenburg, Sweden.

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This study introduces a new method to address under-ascertainment in epidemiologic research by combining capture-recapture and propensity score weighting. The new approach improves the estimation of exposure effects in observational studies with incomplete outcome data.

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Area of Science:

  • Epidemiologic research
  • Biostatistics
  • Public Health

Background:

  • Under-ascertainment, or incomplete case identification, is a major challenge in epidemiologic studies.
  • Existing methods like capture-recapture primarily estimate case numbers, not exposure effects.
  • The impact of under-ascertainment on estimating exposure effects in observational studies remains unclear.

Purpose of the Study:

  • To develop and present a novel ascertainment probability weighting framework.
  • To integrate capture-recapture methods with propensity score weighting for effect estimation.
  • To simultaneously adjust for confounding and under-ascertainment in observational studies.

Main Methods:

  • Proposed a nonparametric estimator for binary outcomes.
  • Combined exposure propensity scores with two conditionally independent outcome measurements.
  • Integrated capture-recapture principles with propensity score weighting.

Main Results:

  • The ascertainment probability weighting method significantly altered estimated associations compared to standard inverse probability weighting.
  • Demonstrated the method's application in a real-world study on healthcare work and COVID-19 testing.
  • Highlighted the critical importance of addressing under-ascertainment in studies with limited outcome data.

Conclusions:

  • Ascertainment probability weighting is crucial for accurate effect estimation when outcome data is incomplete.
  • The proposed framework offers a robust approach to handle both confounding and under-ascertainment.
  • Provided practical guidelines for implementing the method in future research.