
Our client wants to evaluate the effectiveness and safety of a new anticoagulant compared to warfarin in preventing stroke among patients with atrial fibrillation (AF). Instead of clinical trial data, the team uses real-world data (RWD) from claims and EHR sources.
Our programming team anxiously build a cohort of AF patients initiating anticoagulant therapy. They compared stroke incidence between new drug vs. warfarin. While doing so they assess major bleeding events as a safety outcome and finally conduct subgroup analyses (age, renal function, comorbidities).
The outcome analysis shows how RWD bridges the gap between clinical trials and real-world practice, and how SAS programming supports reproducible, regulatory-compliant analyses.
Our client wants to evaluate the effectiveness and safety of a new anticoagulant compared to warfarin in preventing stroke among patients with atrial fibrillation (AF). Instead of clinical trial data, the team uses real-world data (RWD) from claims and EHR sources.
Our programming team anxiously build a cohort of AF patients initiating anticoagulant therapy. They compared stroke incidence between new drug vs. warfarin. While doing so they assess major bleeding events as a safety outcome and finally conduct subgroup analyses (age, renal function, comorbidities).
The outcome analysis shows how RWD bridges the gap between clinical trials and real-world practice, and how SAS programming supports reproducible, regulatory-compliant analyses.
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