Enhancing Safety Signaling: Integrating Clinical Trials and Post-Marketing Adverse Event Reports

Abstract

Problem definition: Negative side effects from taking a drug, termed adverse drug reactions (ADRs), cause numerous emergency room visits and thousands of deaths a year in the U.S. alone.

Ideally, regulators, such as the U.S. Food and Drug Administration (FDA), detect all safety issues before a drug's marketing approval based on clinical trial results. However, trials are often too small or too short in duration to detect rare or slow-developing ADRs. As a result, regulators rely on spontaneous ADR reporting systems (eg, the FDA's FAERS system) to detect potential safety issues. Specifically, they employ this data to generate hypotheses about potential safety issues (termed safety signals) by identifying side effects that occur at a disproportionately high rate in patients taking a drug versus patients taking other drugs for the same condition.

Reliance on biased observational data --- due to selection and reporting differences among patients --- can result in regulators flagging safety issues that are not truly present or in missing real safety issues.

Methodology/results: In this work, we seek to enhance the hypothesis generation step of safety signaling based on spontaneous ADR reporting systems via a Bayesian methodology that combines pre-approval clinical trials and post-approval observational data for multiple ADRs.

We use data from more common adverse events to quantify the direction and magnitude of bias in observational data as compared to clinical trial data and use it to debias the observational data for more rare adverse events. Our key observation is that common and rare ADRs share similar sources of selection and reporting biases. We quantify the benefits of the proposed approach to regulators via both analytical modeling with a stylized dynamic programming model as well as via a detailed numerical evaluation using real-world clinical trials and FAERS data. Numerical results suggest that we can effectively identify scenarios where the proposed approach will improve safety signaling over simpler alternatives, reducing expected Type I and II error costs by 17--41% in these scenarios.

Managerial implications: By leveraging regulators' existing data sources, our approach enhances the hypothesis generation step in post-approval drug surveillance, enabling more accurate and expedited safety signal detection.

Note:

Funding Information: Nothing to declare.

Conflict of Interests: No conflict of interest to declare.

Keywords: Bayesian decision analysis, pharmacovigilance, adverse drug reactions.