Bayesian Posterior Interval Calibration to Improve the Interpretability of Observational Studies

Observational healthcare data offer the potential to estimate causal effects of medical products on a large scale. However, the confidence intervals and p-values produced by observational studies only account for random error and fail to account for systematic error. As a consequence, operating characteristics such as confidence interval coverage and... Show more