Negative controls (NCs) are increasingly used in real-world observational studies to detect residual confounding and systematic bias, but their implementation and reporting remain heterogeneous in applied pharmacoepidemiology. We conducted a methodological scoping review to characterize how NCs were selected, implemented, reported, and interpreted in real-world studies evaluating glucagon-like peptide-1 receptor agonists (GLP-1 RAs) for non-indicated clinical outcomes. We systematically searched PubMed and Embase from database inception through November 2025 and additionally screened reference lists of included studies. Eligible studies used real-world data, applied an observational analytic framework to evaluate GLP-1 RAs, focused on non-indicated outcomes, and explicitly incorporated NCs for bias detection, falsification, validation, or empirical calibration. Among 489 records identified, 42 studies met the inclusion criteria. Most studies were cohort-based and were conducted in diabetes-related populations using electronic health records or administrative claims. Negative control outcomes (NCOs) were the dominant approach, whereas negative control exposures (NCEs), positive controls, and empirical calibration were less common. An explicit rationale for NC selection was reported in most studies, but none of the included studies explicitly discussed the assumptions underlying NC analysis. Most NC findings were reported as null or consistent with expectations, and were primarily used to support validity or robustness rather than to materially alter interpretation. Overall, our findings suggest that the current use of NCs in GLP-1 RA observational research has increased, while standardized implementation and reporting have lagged. Greater transparency regarding why NCs were selected, what source of bias they were intended to probe, how results were interpreted, and whether they changed analytic conclusions may improve the credibility and interpretability of real-world evidence in this rapidly evolving therapeutic area.
