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Extending our calibration framework to a multi-database setting, combining it with OHDSI's Bayesian random-effects meta-analysis framework.

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ohdsi-studies/EvidenceSynthesisWithNegativeControls

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Evidence Synthesis with Negative Controls

Study Status: Started

  • Analytics use case(s): Population-Level Estimation
  • Study type: Methods Research
  • Tags: -
  • Study lead: Martijn Schuemie
  • Study lead forums tag: schuemie
  • Study start date: October 9, 2025
  • Study end date: -
  • Protocol: -
  • Publications: -
  • Results explorer: -

Extending our calibration framework to a multi-database setting, combining it with OHDSI's Bayesian random-effects meta-analysis framework. Because systematic error is likely correlated between databases (e.g. the same confoudning by indication will likely occur in many places), we cannot simply meta-analyse the calibrated estimates. Instead both calibration and evidence synthesis must be combined in a single approach that accounts for these correlations.

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Extending our calibration framework to a multi-database setting, combining it with OHDSI's Bayesian random-effects meta-analysis framework.

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