Design considerations in an active medical product safety monitoring system.
Open accessAbstract
Active medical product monitoring systems, such as the Sentinel System, will utilize electronic healthcare data captured during routine health care. Safety signals that arise from these data may be spurious because of chance or bias, particularly confounding bias, given the observational nature of the data. Applying appropriate monitoring designs can filter out many false-positive and false-negative associations from the outset. Designs can be classified by whether they produce estimates based on between-person or within-person comparisons. In deciding which approach is more suitable for a given monitoring scenario, stakeholders must consider the characteristics of the monitored product, characteristics of the health outcome of interest (HOI), and characteristics of the potential link between these. Specifically, three factors drive design decisions: (i) strength of within-person and between-person confounding; (ii) whether circumstances exist that may predispose to misclassification of exposure or misclassification of the timing of the HOI; and (iii) whether the exposure of interest is predominantly transient or sustained. Additional design considerations include whether to focus on new users, the availability of appropriate active comparators, the presence of an exposure time trend, and the measure of association of interest. When the key assumptions of self-controlled designs are fulfilled (i.e., lack of within-person, time-varying confounding; abrupt HOI onset; and transient exposure), within-person comparisons are preferred because they inherently avoid confounding by fixed factors. The cohort approach generally is preferred in other situations and particularly when timing of exposure or outcome is uncertain because cohort approaches are less vulnerable to biases resulting from misclassification.
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The active comparator design adjusted for confounding from all factors shared across individuals initiating analgesic treatment with opioids and with NSAIDs.
“This approach adjusted for confounding from all factors shared across individuals initiating analgesic treatment with opioids and with NSAIDs (eg, indications for pain treatment)”
From Association of opioid prescription initiation during adolescence and young adulthood with subsequent substance-related morbidity (2020) · cites this paper as [41]
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- Authors
- Gagne, J. J., Fireman, B., Ryan, P. B., Maclure, M., Gerhard, T., Toh, S., Rassen, J. A., Nelson, J. C., & Schneeweiss, S.
- Affiliations
- Brigham and Women's Hospital (US)
- Kaiser Permanente (US)
- Johnson & Johnson (United States) (US)
- Ministry of Health (CA)
- Rutgers, The State University of New Jersey (US)
- Harvard University (US)
- Kaiser Permanente Washington Health Research Institute (US)
- Corresponding author
- Gagne, Joshua J.
- ORCID
- Venue
- Pharmacoepidemiology and Drug Safety
- Publisher
- Wiley
- Published
- 2012-01
- Volume
- 21
- Issue
- S1
- Pages
- 32-40
- ISSN
- 1053-8569 (ISSN-L)
- 1099-1557
- OpenAlex
- W1819504360
- Cited by
- 49
- References
- 51
- Citations by year
- 2026: 3 · 2024: 2 · 2023: 2 · 2022: 3 · 2021: 2 · 2020: 1 · 2019: 4 · 2018: 1 · 2017: 4 · 2016: 3 · 2015: 3 · 2014: 5 · 2013: 11 · 2012: 5
- FWCI
- 10.81
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Pharmacoepidemiology and Drug Safety (publishedVersion)
- PubMed (publishedVersion)
- Funders
- U.S. Department of Health and Human Services (HHSF223200910006I)
- Hamilton Health Sciences Foundation
- Topics
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Toxicology › Pharmacovigilance and Adverse Drug Reactions
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
- Keywords
- Sentinel System, self-controlled designs, cohort studies, confounding, electronic health data, new users
- MeSH terms
- Decision Making, Equipment and Supplies, Humans, Product Surveillance, Postmarketing, Epidemiologic Research Design, Bias, Confounding Factors, Epidemiologic, Databases, Factual, Program Development, Outcome Assessment, Health Care, Electronic Health Records, Drug-Related Side Effects and Adverse Reactions
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