Articlenpj health systems2026
From "negative" trial to positive clinical impact: mitigating eligibility criteria-induced temporal selection bias in emulated clinical trials.
Article in npj health systems, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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0 citing papers in PubMed.
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Authors and funding
16 authors.
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Abstract
While randomized controlled trials (RCTs) guide clinical practice, their completion may also influence real-world treatment patterns. We investigated whether the outcomes of trial emulation differ before and after the publication of the WARCEF (Warfarin versus Aspirin in Reduced Cardiac Ejection Fraction) randomized controlled trial. We emulated the WARCEF trial using EHR data from the Mayo Clinic Platform, comparing Warfarin and Aspirin in patients with heart failure and reduced ejection fraction (HFrEF). Analyses were stratified by the WARCEF completion date (July 2014), using intention-to-treat (ITT) frameworks. For the ITT analysis, the cohort size before 2014 consisted of 37,225 patients on Aspirin and 2022 on Warfarin, while after 2014, the cohort included 26,192 patients on Aspirin and 1080 on Warfarin. No significant treatment difference was observed before July 2014, 1.025 (95% CI: 0.6144 - 1.709, p = 0.9255), consistent with the findings of the WARCEF trial. However, after trial completion, Warfarin was associated with increased risk, 2.181 (95% CI: 1.676 - 2.839, p < 0.001). These findings initially suggested that the completion of the WARCEF trial and subsequent guideline updates were associated with changes in observed treatment effects in real-world emulation. However, further analyses indicate that the observed differences are primarily driven by the application of specific eligibility criteria rather than the trial completion date itself. In particular, the inclusion of restrictive eligibility criteria alters cohort composition and event timing, introducing selection bias that materially affects treatment effect estimates. This highlights that, in trial emulation studies, eligibility criteria can have a greater impact on observed outcomes than temporal stratification alone. Accurate emulation therefore requires careful alignment of eligibility definitions with the original trial design, as inappropriate or overly restrictive criteria may induce selection bias and distort causal inference, independent of trial publication timing.
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