Learning Health Systems · Published 2026-06-07 · DOI 10.1002/lrh2.70098
Harris Butler, John D. Rice, Nichole E. Carlson, Elaine H. Morrato
ABSTRACT Introduction Active post‐marketing surveillance of prescribing behavior of high‐risk drugs may provide early warning of unforeseen issues in a population, yet analysis approaches for surveillance using real‐world data are underdeveloped. This paper evaluates a modified statistical process control (SPC) method for surveillance of risk minimization measures derived from administrative claims data. The approach detects changes in population‐level prescribing behaviors and informs investigators of the timing and nature of any detected changes. Methods We investigated prescription drug claims for tapentadol extended release (ER) from the Colorado All‐Payers Claims Database (2012–2019). The cohort was 2702 unique patients receiving their first prescriptions of tapentadol ER (an opiate with an FDA Risk Evaluation and Mitigation Strategy). The risk minimization measures were the prescribing rate and the proportion of prescriptions with appropriate dosing. A statistical model was fitted to data from January–December 2012 to establish a stable baseline and then updated biweekly through June 2019. We conducted surveillance with our modified SPC method and a classical SPC method, controlling the false alarm rate to 0.005 for each, and compared how detections aligned with external policy actions. Results Our method detected two periods of unusual prescribing behavior beginning in March 2015 (p < 0.005) and April 2016 (p < 0.005). The classical method detected a single period in October 2016 (p < 0.005). Our detections aligned with risk minimization activities in Colorado; the classical method aligned with only the second activity, 6 months later. Conclusion Our modified SPC method, which monitors model misspecification rather than raw prescribing data, detects more periods of changing behavior that better temporally align with risk minimization actions. This tool may be useful for regulatory agencies to independently monitor for emerging risks and prescribing trends to complement REMS assessments.
Abstract from DOAJ. Public domain (CC0 1.0).
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Butler, H., Rice, J., Carlson, N., et al. (2026). Surveillance of Pharmaceutical Risk‐Mitigation Behavior: Applying and Comparing Statistical Process Control Methods Using Real World Data. Learning Health Systems. https://doi.org/10.1002/lrh2.70098