Recent years have witnessed new innovation in Bayesian techniques to adjust for unmeasured confounding. A challenge with existing methods is that the user is often required to elicit prior distributions for high-dimensional parameters that model competing bias scenarios. This can render the methods unwieldy. In this paper, we propose a novel methodology to adjust for unmeasured confounding that...

PURPOSE Signal detection methods have been used extensively in postmarket surveillance to identify elevated risks of adverse events associated with medical products (drugs, vaccines, and devices). However, current popular disproportionality methods ignore useful information such as trends when the data are aggregated over time for signal detection. METHODS In this paper, we applied change poi...

The reported association between antibiotic prescribing and resistance may be subject to bias or confounding. Bias describes any effect at any stage of investigation or inference tending to produce results that depart systematically from the true value. A confounding variable is one that is associated independently with both exposure and outcome. Confounding variables may create an apparent ass...

The increased availability of digital data from electronic health records (EHR) systems has created heightened interest in their use in pharmacoepidemiology. Sources of such data have been somewhat restrictive and not generally accessible to scientists because of institutional policies. However, these data-access restrictions appear to be diminishing. The recent thrust by the Institute ofMedici...