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Adjustment for 'Prescriber Type' in Pharmacoepidemiological Analyses.

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Title: Adjustment for 'Prescriber Type' in Pharmacoepidemiological Analyses.
Authors: Abbasi, Saad Hanif1 (AUTHOR) shabbasi@health.sdu.dk, Hallas, Jesper1 (AUTHOR), Jensen, Peter Bjødstrup1 (AUTHOR), Al‐Jasim, Hassan1 (AUTHOR), Pottegård, Anton1 (AUTHOR)
Source: Basic & Clinical Pharmacology & Toxicology. Nov2025, Vol. 137 Issue 5, p1-7. 7p.
Subject Terms: *PHARMACOEPIDEMIOLOGY, *CONFOUNDING variables, *ANTICOAGULANTS, *CORRECTION factors, *STATISTICAL bias, *ISCHEMIC stroke, *MEDICAL personnel, *MEDICAL registries
Abstract: The type of prescriber typically fulfils the criteria for confounding, as it is associated both with the exposure (e.g., prescriber types may differ in their choice of first‐line treatment) and with the outcome (as different types of prescribers often treat patients with different disease severity). Additionally, the type of prescriber may correlate with other factors such as treatment adherence, surveillance or coding practices. Although information on the type of prescriber is often available in healthcare registries, it is very rarely employed to control for confounding in pharmacoepidemiological analyses. Here, we argue the potential value in adjusting for the prescriber type in pharmacoepidemiological studies. In an applied example, we conducted a cohort study using Danish healthcare registers of the risk of ischemic stroke associated with the use of direct oral anticoagulants (DOACs) compared to warfarin. We found a hazard ratio (HR) of 0.95 (95% CI: 0.90–1.01) for DOACs versus warfarin when adjusting only for age and sex. Further adjustment for prescriber type showed an effect of similar magnitude (HR 0.92; 95% CI: 0.87–0.98). However, interaction testing and stratified analyses confirmed prescriber type as an effect modifier. Future studies are needed to clarify the role of adjusting for prescriber type across other use cases and healthcare settings. Plain Language Summary: Different prescribers may choose different treatments or treat patients with more severe conditions, making the type of prescriber a so‐called confounder, that is, a factor that, if not controlled for, will lead to spurious results. However, information on prescriber type is only very rarely used inpharmacoepidemiology studies. We tried to illustrate the value of considering prescriber type by studying the effectiveness of two types of anticoagulants to prevent strokes. Although adjustment for prescriber type only had minimal effect on the estimates obtained, analysis conducted within hospital prescribers and general practitioners separately showed divergent results. We need more studies to fully determine when adjusting for prescriber type is important in pharmacoepidemiology studies. [ABSTRACT FROM AUTHOR]
Database: Academic Search Index
Description
Abstract:The type of prescriber typically fulfils the criteria for confounding, as it is associated both with the exposure (e.g., prescriber types may differ in their choice of first‐line treatment) and with the outcome (as different types of prescribers often treat patients with different disease severity). Additionally, the type of prescriber may correlate with other factors such as treatment adherence, surveillance or coding practices. Although information on the type of prescriber is often available in healthcare registries, it is very rarely employed to control for confounding in pharmacoepidemiological analyses. Here, we argue the potential value in adjusting for the prescriber type in pharmacoepidemiological studies. In an applied example, we conducted a cohort study using Danish healthcare registers of the risk of ischemic stroke associated with the use of direct oral anticoagulants (DOACs) compared to warfarin. We found a hazard ratio (HR) of 0.95 (95% CI: 0.90–1.01) for DOACs versus warfarin when adjusting only for age and sex. Further adjustment for prescriber type showed an effect of similar magnitude (HR 0.92; 95% CI: 0.87–0.98). However, interaction testing and stratified analyses confirmed prescriber type as an effect modifier. Future studies are needed to clarify the role of adjusting for prescriber type across other use cases and healthcare settings. Plain Language Summary: Different prescribers may choose different treatments or treat patients with more severe conditions, making the type of prescriber a so‐called confounder, that is, a factor that, if not controlled for, will lead to spurious results. However, information on prescriber type is only very rarely used inpharmacoepidemiology studies. We tried to illustrate the value of considering prescriber type by studying the effectiveness of two types of anticoagulants to prevent strokes. Although adjustment for prescriber type only had minimal effect on the estimates obtained, analysis conducted within hospital prescribers and general practitioners separately showed divergent results. We need more studies to fully determine when adjusting for prescriber type is important in pharmacoepidemiology studies. [ABSTRACT FROM AUTHOR]
ISSN:17427835
DOI:10.1111/bcpt.70126