In pharmacoepidemiological analysis, one covariate that might act as a confounder is the type of prescriber issuing a prescription. The type of prescriber typically fulfills 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. While information on the type of prescriber is often available to researchers, it is rarely employed to control for confounding in pharmacoepidemiological analyses. In an applied example, we conducted a cohort study using Danish healthcare registers from 2011 to 2018 to assess 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 0.93; 95% CI: 0.87-0.98). However, in stratified analyses, we observed higher estimates in the group of general practitioners (HR 1.06; 95% CI: 0.94-1.2) compared to hospital prescribers (HR 0.88; 95% CI: 0.82-0.95), indicating potential effect modification. This highlights the potential value of prescriber type as an important covariate in pharmacoepidemiological analyses. Further research is needed to fully establish the importance of prescriber type in such analyses.