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Optimising the case-crossover design for use in shared exposure settings.

T Braeye1,2, N Hens2,3

  • 1Department of Public Health and Surveillance, Sciensano, Brussels, Belgium.

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|May 5, 2020
PubMed
Summary
This summary is machine-generated.

Referent selection strategies (RSS) in case-crossover studies impact bias and confounding. Careful selection is crucial for accurate results, especially during outbreaks, as simple referent selection may not fully control time-varying confounding.

Keywords:
Case-crossover studyconditional logistic regressionsimulation study

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Environmental Health

Background:

  • Case-crossover studies compare exposure during risk periods to referent periods.
  • Referent selection strategy (RSS) is key in this self-controlled design.
  • Previous research focused on systematic bias from RSS, but time-varying confounding is also critical.

Purpose of the Study:

  • To investigate how RSS influences the number of referents, overdispersion, and time-varying confounding.
  • To evaluate the statistical power and bias susceptibility of different RSS.
  • To explore solutions for mitigating bias in case-crossover studies.

Main Methods:

  • A simulation study using meteorological data and Legionnaires' disease.
  • Randomization of events and exposure time series.
  • Exploration of time-stratified and fixed bidirectional RSS.
  • Investigation of outbreak data impact on coefficient estimation.

Main Results:

  • Referent selection alone is insufficient to control time-varying confounding bias.
  • Confounding bias is harder to detect than systematic bias.
  • Outbreaks can affect coefficient estimation.

Conclusions:

  • Different RSS have varying impacts on bias, power, and confounding.
  • Solutions like parameter variation, outlier removal, and data aggregation can help mitigate bias.
  • The simulation framework aids researchers in detecting and avoiding bias in case-crossover studies.