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Bayesian data fusion: Probabilistic sensitivity analysis for unmeasured confounding using informative priors based on

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Bayesian causal inference and a new Bayesian data fusion method improve policy evaluation for interventions. These methods address unmeasured confounding for better causal inference in complex settings.

Keywords:
causal inferencedata fusiong-formulamediationracial disparitiesunmeasured confounding

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Area of Science:

  • Causal Inference
  • Biostatistics
  • Epidemiology

Background:

  • Bayesian causal inference provides a robust framework for evaluating interventions.
  • Existing methods like the Bayesian g-formula handle dynamic treatment regimes.
  • Challenges remain in addressing unmeasured confounding in mediation analysis.

Purpose of the Study:

  • To extend the Bayesian g-formula for population-level causal quantities under dynamic regimes.
  • To introduce Bayesian data fusion (BDF) for sensitivity analysis with external data.
  • To evaluate BDF's performance against frequentist methods for unmeasured confounding.

Main Methods:

  • Developed a general Bayesian approach for dynamic and stochastic treatment regimes.
  • Proposed Bayesian data fusion (BDF) for probabilistic sensitivity analysis using external confounder data.
  • Conducted a simulation study comparing BDF with frequentist bias correction methods.
  • Applied methods to analyze colorectal cancer survival disparities.

Main Results:

  • The proposed Bayesian approach effectively estimates causal quantities for complex interventions.
  • Bayesian data fusion corrects for unmeasured mediator-outcome confounding when data are transportable.
  • BDF demonstrated comparable or superior performance to frequentist methods in simulations.
  • Analysis revealed the role of cancer diagnosis stage in Black-White survival disparities.

Conclusions:

  • Bayesian causal inference, particularly with BDF, offers a powerful tool for policy evaluation and handling unmeasured confounding.
  • BDF enables causal inference from external data without compromising privacy.
  • These methods advance the ability to make causal claims in observational studies, particularly in health disparities research.