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

Bonander, Carl ; Nilsson, Anton LU ; Li, Huiqi ; Sharma, Shambhavi ; Nwaru, Chioma ; Gisslén, Magnus ; Lindh, Magnus ; Hammar, Niklas ; Björk, Jonas LU orcid and Nyberg, Fredrik (2024) In Epidemiology 35(3). p.340-348
Abstract

Outcome under-ascertainment, characterized by the incomplete identification or reporting of cases, poses a substantial challenge in epidemiologic research. While capture-recapture methods can estimate unknown case numbers, their role in estimating exposure effects in observational studies is not well established. This paper presents an ascertainment probability weighting framework that integrates capture-recapture and propensity score weighting. We propose a nonparametric estimator of effects on binary outcomes that combines exposure propensity scores with data from two conditionally independent outcome measurements to simultaneously adjust for confounding and under-ascertainment. Demonstrating its practical application, we apply the... (More)

Outcome under-ascertainment, characterized by the incomplete identification or reporting of cases, poses a substantial challenge in epidemiologic research. While capture-recapture methods can estimate unknown case numbers, their role in estimating exposure effects in observational studies is not well established. This paper presents an ascertainment probability weighting framework that integrates capture-recapture and propensity score weighting. We propose a nonparametric estimator of effects on binary outcomes that combines exposure propensity scores with data from two conditionally independent outcome measurements to simultaneously adjust for confounding and under-ascertainment. Demonstrating its practical application, we apply the method to estimate the relationship between health care work and coronavirus disease 2019 testing in a Swedish region. We find that ascertainment probability weighting greatly influences the estimated association compared to conventional inverse probability weighting, underscoring the importance of accounting for under-ascertainment in studies with limited outcome data coverage. We conclude with practical guidelines for the method's implementation, discussing its strengths, limitations, and suitable scenarios for application.

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author
; ; ; ; ; ; ; ; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Information bias, Misclassification, Register completeness, Sensitivity, Under-coverage
in
Epidemiology
volume
35
issue
3
pages
9 pages
publisher
Wolters Kluwer
external identifiers
  • pmid:38442421
  • scopus:85190851009
ISSN
1044-3983
DOI
10.1097/EDE.0000000000001717
project
Improved preparedness for future pandemics and other health crises through large-scale disease surveillance
language
English
LU publication?
yes
id
366fb4b9-c19a-4c59-990b-c39df976d9b3
date added to LUP
2025-01-13 13:19:51
date last changed
2025-07-01 03:22:58
@article{366fb4b9-c19a-4c59-990b-c39df976d9b3,
  abstract     = {{<p>Outcome under-ascertainment, characterized by the incomplete identification or reporting of cases, poses a substantial challenge in epidemiologic research. While capture-recapture methods can estimate unknown case numbers, their role in estimating exposure effects in observational studies is not well established. This paper presents an ascertainment probability weighting framework that integrates capture-recapture and propensity score weighting. We propose a nonparametric estimator of effects on binary outcomes that combines exposure propensity scores with data from two conditionally independent outcome measurements to simultaneously adjust for confounding and under-ascertainment. Demonstrating its practical application, we apply the method to estimate the relationship between health care work and coronavirus disease 2019 testing in a Swedish region. We find that ascertainment probability weighting greatly influences the estimated association compared to conventional inverse probability weighting, underscoring the importance of accounting for under-ascertainment in studies with limited outcome data coverage. We conclude with practical guidelines for the method's implementation, discussing its strengths, limitations, and suitable scenarios for application.</p>}},
  author       = {{Bonander, Carl and Nilsson, Anton and Li, Huiqi and Sharma, Shambhavi and Nwaru, Chioma and Gisslén, Magnus and Lindh, Magnus and Hammar, Niklas and Björk, Jonas and Nyberg, Fredrik}},
  issn         = {{1044-3983}},
  keywords     = {{Information bias; Misclassification; Register completeness; Sensitivity; Under-coverage}},
  language     = {{eng}},
  number       = {{3}},
  pages        = {{340--348}},
  publisher    = {{Wolters Kluwer}},
  series       = {{Epidemiology}},
  title        = {{A Capture-Recapture-based Ascertainment Probability Weighting Method for Effect Estimation with Under-ascertained Outcomes}},
  url          = {{http://dx.doi.org/10.1097/EDE.0000000000001717}},
  doi          = {{10.1097/EDE.0000000000001717}},
  volume       = {{35}},
  year         = {{2024}},
}