{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"institution":[{"name":"medRxiv"}],"indexed":{"date-parts":[[2026,9,15]],"date-time":"2026-09-15T11:33:27Z","timestamp":1789472007316,"version":"build-2803163510"},"posted":{"date-parts":[[2026,7,13]]},"group-title":"Epidemiology","reference-count":24,"publisher":"openRxiv","license":[{"start":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T00:00:00Z","timestamp":1783900800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"accepted":{"date-parts":[[2026,7,13]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                <jats:p>\n                  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\n                  <jats:italic>residual bias<\/jats:italic>\n                  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\n                  <jats:italic>p<\/jats:italic>\n                  -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\u2013glucose 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.\n                <\/jats:p>\n                <jats:sec>\n                  <jats:title>Disclaimer<\/jats:title>\n                  <jats:p>The contents are solely the responsibility of the authors and do not necessarily represent the official views of, or an endorsement by, Food and Drug Administration (FDA)\/Department of Health and Human Services (HHS) or the U.S. Government.<\/jats:p>\n                <\/jats:sec>","DOI":"10.64898\/2026.07.08.26357550","type":"posted-content","created":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T05:55:21Z","timestamp":1784008521000},"source":"Crossref","is-referenced-by-count":4,"title":["Distributional Diagnosis and Calibration with Negative Controls for Outcome-wide Real-world Evidence"],"prefix":"10.64898","author":[{"given":"Huiyuan","family":"Wang","sequence":"first","affiliation":[{"name":"The Center for Health AI and Synthesis of Evidence (CHASE), University of Pennsylvania, Philadelphia, PA, USA"},{"name":"Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7176-249X","authenticated-orcid":false,"given":"Bingyu","family":"Zhang","sequence":"additional","affiliation":[{"name":"The Center for Health AI and Synthesis of Evidence (CHASE), University of Pennsylvania, Philadelphia, PA, USA"},{"name":"The Graduate Group in Applied Mathematics and Computational Science, School of Arts and Sciences, University of Pennsylvania, Philadelphia, PA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuqing","family":"Lei","sequence":"additional","affiliation":[{"name":"The Center for Health AI and Synthesis of Evidence (CHASE), University of Pennsylvania, Philadelphia, PA, USA"},{"name":"Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yiwen","family":"Lu","sequence":"additional","affiliation":[{"name":"The Center for Health AI and Synthesis of Evidence (CHASE), University of Pennsylvania, Philadelphia, PA, USA"},{"name":"The Graduate Group in Applied Mathematics and Computational Science, School of Arts and Sciences, University of Pennsylvania, Philadelphia, PA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dazheng","family":"Zhang","sequence":"additional","affiliation":[{"name":"The Center for Health AI and Synthesis of Evidence (CHASE), University of Pennsylvania, Philadelphia, PA, USA"},{"name":"Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinyao","family":"Jian","sequence":"additional","affiliation":[{"name":"The Center for Health AI and Synthesis of Evidence (CHASE), University of Pennsylvania, Philadelphia, PA, USA"},{"name":"Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuru","family":"Zhu","sequence":"additional","affiliation":[{"name":"The Center for Health AI and Synthesis of Evidence (CHASE), University of Pennsylvania, Philadelphia, PA, USA"},{"name":"Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenjie","family":"Hu","sequence":"additional","affiliation":[{"name":"The Center for Health AI and Synthesis of Evidence (CHASE), University of Pennsylvania, Philadelphia, PA, USA"},{"name":"Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haitao","family":"Chu","sequence":"additional","affiliation":[{"name":"Statistical Research and Data Science Center, Pfizer Inc., New York, NY, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yong","family":"Chen","sequence":"additional","affiliation":[{"name":"Statistical Research and Data Science Center, Pfizer Inc., New York, NY, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9818-479X","authenticated-orcid":false,"given":"Marc A.","family":"Suchard","sequence":"additional","affiliation":[{"name":"Observational Health Data Sciences and Informatics, New York, NY, USA"},{"name":"Department of Biostatistics, University of California, Los Angeles, CA, USA"},{"name":"VA Informatics and Computing Infrastructure, US Department of Veterans Affairs, Salt Lake City, UT, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Patrick B.","family":"Ryan","sequence":"additional","affiliation":[{"name":"Observational Health Data Sciences and Informatics, New York, NY, USA"},{"name":"Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, USA"},{"name":"Global Epidemiology Organization, Johnson & Johnson, Titusville, NJ, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"George","family":"Hripcsak","sequence":"additional","affiliation":[{"name":"Observational Health Data Sciences and Informatics, New York, NY, USA"},{"name":"Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"David A.","family":"Asch","sequence":"additional","affiliation":[{"name":"Leonard Davis Institute of Health Economics, Philadelphia, PA, USA"},{"name":"Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA"},{"name":"Penn Center for Health Incentives and Behavioral Economics, University of Pennsylvania, Philadelphia, PA, USA"},{"name":"Wharton School, University of Pennsylvania, Philadelphia, PA, USA"},{"name":"Division of General Internal Medicine, University of Pennsylvania, Philadelphia, PA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yun","family":"Lu","sequence":"additional","affiliation":[{"name":"Center for Biologics Evaluation and Research, Food and Drug Administration, Silver Spring, MD, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bin","family":"Yu","sequence":"additional","affiliation":[{"name":"Department of Statistics, University of California, Berkeley, Berkeley, CA, USA"},{"name":"Department of Electrical Engineering and Computer Science, University of California, Berkeley, Berkeley, CA, USA"},{"name":"Center for Computational Biology, University of California, Berkeley, Berkeley, CA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Martijn J.","family":"Schuemie","sequence":"additional","affiliation":[{"name":"Observational Health Data Sciences and Informatics, New York, NY, USA"},{"name":"Department of Biostatistics, University of California, Los Angeles, CA, USA"},{"name":"Global Epidemiology Organization, Johnson & Johnson, Titusville, NJ, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yumou","family":"Qiu","sequence":"additional","affiliation":[{"name":"School of Mathematical Sciences and Center for Statistical Science, Peking University, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0835-0788","authenticated-orcid":false,"given":"Yong","family":"Chen","sequence":"additional","affiliation":[{"name":"The Center for Health AI and Synthesis of Evidence (CHASE), University of Pennsylvania, Philadelphia, PA, USA"},{"name":"Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA"},{"name":"The Graduate Group in Applied Mathematics and Computational Science, School of Arts and Sciences, University of Pennsylvania, Philadelphia, PA, USA"},{"name":"Leonard Davis Institute of Health Economics, Philadelphia, PA, USA"},{"name":"Penn Institute for Biomedical Informatics (IBI), Philadelphia, PA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"54368","reference":[{"key":"2026071610150916000_2026.07.08.26357550v1.1","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMoa1607141"},{"key":"2026071610150916000_2026.07.08.26357550v1.2","doi-asserted-by":"publisher","DOI":"10.1111\/dom.16364"},{"key":"2026071610150916000_2026.07.08.26357550v1.3","unstructured":"Truveta. GLP-1 RA Prescription Trends, September 2025, 2025."},{"key":"2026071610150916000_2026.07.08.26357550v1.4","doi-asserted-by":"publisher","DOI":"10.1001\/jama.2023.19574"},{"issue":"1","key":"2026071610150916000_2026.07.08.26357550v1.5","doi-asserted-by":"crossref","first-page":"168","DOI":"10.1038\/s41591-023-02672-2","article-title":"Association of semaglutide with risk of suicidal ideation in a real-world cohort","volume":"30","year":"2024","journal-title":"Nature Medicine"},{"key":"2026071610150916000_2026.07.08.26357550v1.6","doi-asserted-by":"publisher","DOI":"10.7326\/ANNALS-24-01347"},{"key":"2026071610150916000_2026.07.08.26357550v1.7","doi-asserted-by":"publisher","DOI":"10.1097\/EDE.0b013e3181d61eeb"},{"key":"2026071610150916000_2026.07.08.26357550v1.8","doi-asserted-by":"publisher","DOI":"10.1001\/jama.2016.17700"},{"key":"2026071610150916000_2026.07.08.26357550v1.9","doi-asserted-by":"publisher","DOI":"10.2307\/2531497"},{"issue":"5","key":"2026071610150916000_2026.07.08.26357550v1.10","first-page":"1863","article-title":"Confounder adjustment in multiple hypothesis testing","volume":"45","year":"2017","journal-title":"The Annals of Statistics"},{"key":"2026071610150916000_2026.07.08.26357550v1.11","doi-asserted-by":"publisher","DOI":"10.1093\/biomet\/asy038"},{"issue":"543","key":"2026071610150916000_2026.07.08.26357550v1.12","doi-asserted-by":"crossref","first-page":"1953","DOI":"10.1080\/01621459.2021.2023551","article-title":"Identifying effects of multiple treatments in the presence of unmeasured confounding","volume":"118","year":"2023","journal-title":"Journal of the American Statistical Association"},{"issue":"1","key":"2026071610150916000_2026.07.08.26357550v1.13","doi-asserted-by":"crossref","first-page":"e212","DOI":"10.1017\/cts.2023.635","article-title":"A causal roadmap for generating high-quality real-world evidence","volume":"7","year":"2023","journal-title":"Journal of Clinical and Translational Science"},{"issue":"4","key":"2026071610150916000_2026.07.08.26357550v1.14","doi-asserted-by":"crossref","first-page":"927","DOI":"10.1002\/cpt.3563","article-title":"Advancing principled pharmacoepidemiologic research to support regulatory and healthcare decision making: the era of real-world evidence","volume":"117","year":"2025","journal-title":"Clinical Pharmacology & Therapeutics"},{"key":"2026071610150916000_2026.07.08.26357550v1.15","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbi.2016.12.005"},{"key":"2026071610150916000_2026.07.08.26357550v1.16","doi-asserted-by":"publisher","DOI":"10.1016\/j.jacc.2024.05.069"},{"key":"2026071610150916000_2026.07.08.26357550v1.17","doi-asserted-by":"publisher","DOI":"10.1093\/aje\/kwh090"},{"key":"2026071610150916000_2026.07.08.26357550v1.18","doi-asserted-by":"publisher","DOI":"10.1093\/oxfordjournals.aje.a114593"},{"key":"2026071610150916000_2026.07.08.26357550v1.19","doi-asserted-by":"publisher","DOI":"10.1198\/016214504000001880"},{"issue":"2","key":"2026071610150916000_2026.07.08.26357550v1.20","first-page":"319","article-title":"Nonparametric bounds on treatment effects","volume":"80","year":"1990","journal-title":"The American Economic Review"},{"key":"2026071610150916000_2026.07.08.26357550v1.21","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.2012.742018"},{"issue":"4","key":"2026071610150916000_2026.07.08.26357550v1.22","first-page":"561","article-title":"Instrumental variable estimation with a dtochastic monotonicity assumption","volume":"32","year":"2017","journal-title":"Statistical Science"},{"key":"2026071610150916000_2026.07.08.26357550v1.23","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.2013.869224"},{"issue":"1","key":"2026071610150916000_2026.07.08.26357550v1.24","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1093\/jrsssb\/qkae040","article-title":"Multivariate, heteroscedastic empirical bayes via nonparametric maximum likelihood","volume":"87","year":"2025","journal-title":"Journal of the Royal Statistical Society Series B: Statistical Methodology"}],"container-title":[],"original-title":[],"link":[{"URL":"https:\/\/syndication.highwire.org\/content\/doi\/10.64898\/2026.07.08.26357550","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T17:15:21Z","timestamp":1784222121000},"score":1,"resource":{"primary":{"URL":"http:\/\/medrxiv.org\/lookup\/doi\/10.64898\/2026.07.08.26357550"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,13]]},"references-count":24,"URL":"https:\/\/doi.org\/10.64898\/2026.07.08.26357550","relation":{},"subject":[],"published":{"date-parts":[[2026,7,13]]},"subtype":"preprint"}}