Related Experiment Video
Updated: Jun 11, 2026
Microbial Control and Monitoring Strategies for Cleanroom Environments and Cellular Therapies
Published on: March 17, 2023
Surveillance of Pharmaceutical Risk-Mitigation Behavior: Applying and Comparing Statistical Process Control Methods
Harris Butler1,2, John D Rice1,3, Nichole E Carlson1
1Department of Biostatistics and Informatics University of Colorado Anschutz Medical Campus Aurora Colorado USA.
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.
Related Concept Videos
01:15Introduction to Statistical Process Control
01:19Pharmacovigilance
This process, termed pharmacovigilance, aims to detect, evaluate, and minimize harmful effects related to medication use. The data collection for pharmacovigilance depends on spontaneous reporting systems, where healthcare professionals or patients voluntarily report suspected ADRs.
In some cases, there...
01:23Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast, controlled...
01:12Analysis of Population Pharmacokinetic Data
01:03Drug Control Governance: Regulatory Bodies and Their Impact
01:05Quality Control
Quality control helps track data, visualize trends, and identify variations, making it easier to detect deviations that may affect the accuracy of an analysis. One way to do this is by generating a quality control chart, which...