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Analyzing self-controlled case series data when case confirmation rates are estimated from an internal validation

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Vaccine safety studies using electronic health records face challenges with adverse event misclassification. Multiple imputation analysis of self-controlled case series data offers a robust solution, improving accuracy and reliability.

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Pharmacovigilance

Background:

  • Electronic health records (EHRs) are crucial for vaccine safety surveillance.
  • Observational studies using EHRs face challenges like confounding and adverse event misclassification.
  • The self-controlled case series (SCCS) design is used to address confounding in vaccine safety research.

Purpose of the Study:

  • To evaluate four methods for analyzing SCCS data when adverse event confirmation rates are estimated from validation samples.
  • To compare the performance of observed cases, confirmed cases only, known confirmation rate, and multiple imputation (MI) approaches.
  • To identify the most reliable method for handling misclassified adverse events in vaccine safety studies.

Main Methods:

  • A simulation study was conducted to assess type I error rates, percent bias, and empirical power.
  • Four analytical approaches for SCCS data were evaluated: observed cases, confirmed cases only, known confirmation rate, and multiple imputation.
  • Confirmation rates were estimated from an internal validation sample.

Main Results:

  • Approaches using observed cases, confirmed cases only, or known confirmation rates can inflate type I error, bias estimates, and reduce statistical power when misclassification is present.
  • The multiple imputation (MI) approach effectively accounts for uncertainty in estimated confirmation rates.
  • MI yielded proper type I error rates, largely unbiased point estimates, accurate variance estimates, and appropriate statistical power.

Conclusions:

  • Standard methods for analyzing SCCS data with misclassified adverse events can lead to unreliable vaccine safety findings.
  • Multiple imputation is a superior method for analyzing SCCS data with estimated confirmation rates, offering improved accuracy and statistical validity.
  • This MI approach enhances the reliability of vaccine safety signal detection from EHR data.