medRxiv : the preprint server for health sciencesJul 30, 2026

Using Negative Controls to Improve Real-World Outcome Studies

GLP-1 Therapies Weekly Brief ↗PubMed ↗DOI ↗OA ↗

Updated Aug 3, 2026

Abstract

Analysis of real-world data from 152.7 million patients reveals substantial systematic error in comparative risk profiling of glucagon-like peptide-1 receptor agonists (GLP-1RAs).

Glucagon-like peptide-1 receptor agonists (GLP-1RAs) may have diverse effects across various organ systems.
Residual bias can persist even after adjustments for observed confounders, potentially distorting effect estimates.
A new method called distributional diagnosis and calibration (DC) uses negative control outcomes to identify and adjust for this residual bias.
DC can evaluate the uniformity of statistical significance and the reliability of confidence intervals for primary outcomes.
In comparisons of GLP-1RAs and sodium-glucose cotransporter 2 inhibitors across 15 outcomes, DC diagnostics indicated significant systematic error that varied by method.
Calibration through DC improved the accuracy and reliability of effect estimates for clinical outcomes.

Simplified

Glucagon-like peptide-1 receptor agonists (GLP-1RAs) have been linked to heterogeneous, potentially pleiotropic effects across organ systems, motivating outcome-wide comparative risk profiling in real-world data. A central challenge in such analyses is residual bias that remains after adjustment for observed confounders, which can distort effect estimates and mis-calibrate uncertainty. We present distributional diagnosis and calibration (DC), which uses panels of negative control outcomes (NCOs) to diagnose residual bias and calibrate uncertainty. DC evaluates null behavior via p -value uniformity and empirical coverage across NCOs, and uses the empirical distribution of NCO effect estimates to calibrate confidence intervals for prespecified primary outcomes. DC is modular: it can wrap around commonly used causal inference methods and operates directly on summary statistics, supporting collaborative research under data-sharing constraints. Using electronic health records from a large U.S. clinical research network (152.7 million patients), we compared GLP-1RAs with sodium-glucose cotransporter 2 inhibitors across 15 prespecified outcomes spanning cardiovascular, mental health, and genitourinary domains using four causal estimators. Across outcomes and methods, DC diagnostics revealed substantial and method-dependent residual systematic error. DC calibration attenuated systematic error signals observed in negative controls and yielded more stable, better-calibrated estimates for clinical outcomes, supporting DC as a practical strategy to strengthen the credibility of outcome-wide real-world CER.

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Using Negative Controls to Improve Real-World Outcome Studies

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