Large-scale evidence generation and evaluation across a network of databases for type 2 diabetes mellitus (LEGEND-T2DM): a protocol for a series of multinational, real-world comparative cardiovascular effectiveness and safety studies
Introduction Therapeutic options for type 2 diabetes mellitus (T2DM) have expanded over the last decade with the emergence of cardioprotective novel agents, but without such data for older drugs, leaving a critical gap in our understanding of the relative effects of T2DM agents on cardiovascular ris...
Saved in:
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMJ Publishing Group
2022-06-01
|
| Series: | BMJ Open |
| Online Access: | https://bmjopen.bmj.com/content/12/6/e057977.full |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832583734055927808 |
|---|---|
| author | Rohan Khera Yuan Lu Harlan M Krumholz Martijn J Schuemie Ruijun Chen George Hripcsak Marc A Suchard Patrick B Ryan Anna Ostropolets |
| author_facet | Rohan Khera Yuan Lu Harlan M Krumholz Martijn J Schuemie Ruijun Chen George Hripcsak Marc A Suchard Patrick B Ryan Anna Ostropolets |
| author_sort | Rohan Khera |
| collection | DOAJ |
| description | Introduction Therapeutic options for type 2 diabetes mellitus (T2DM) have expanded over the last decade with the emergence of cardioprotective novel agents, but without such data for older drugs, leaving a critical gap in our understanding of the relative effects of T2DM agents on cardiovascular risk.Methods and analysis The large-scale evidence generations across a network of databases for T2DM (LEGEND-T2DM) initiative is a series of systematic, large-scale, multinational, real-world comparative cardiovascular effectiveness and safety studies of all four major second-line anti-hyperglycaemic agents, including sodium–glucose co-transporter-2 inhibitor, glucagon-like peptide-1 receptor agonist, dipeptidyl peptidase-4 inhibitor and sulfonylureas. LEGEND-T2DM will leverage the Observational Health Data Sciences and Informatics (OHDSI) community that provides access to a global network of administrative claims and electronic health record data sources, representing 190 million patients in the USA and about 50 million internationally. LEGEND-T2DM will identify all adult, patients with T2DM who newly initiate a traditionally second-line T2DM agent. Using an active comparator, new-user cohort design, LEGEND-T2DM will execute all pairwise class-versus-class and drug-versus-drug comparisons in each data source, producing extensive study diagnostics that assess reliability and generalisability through cohort balance and equipoise to examine the relative risk of cardiovascular and safety outcomes. The primary cardiovascular outcomes include a composite of major adverse cardiovascular events and a series of safety outcomes. The study will pursue data-driven, large-scale propensity adjustment for measured confounding, a large set of negative control outcome experiments to address unmeasured and systematic bias.Ethics and dissemination The study ensures data safety through a federated analytic approach and follows research best practices, including prespecification and full disclosure of results. LEGEND-T2DM is dedicated to open science and transparency and will publicly share all analytic code from reproducible cohort definitions through turn-key software, enabling other research groups to leverage our methods, data and results to verify and extend our findings. |
| format | Article |
| id | doaj-art-a70896859c874bc1897535adc3a295bd |
| institution | Kabale University |
| issn | 2044-6055 |
| language | English |
| publishDate | 2022-06-01 |
| publisher | BMJ Publishing Group |
| record_format | Article |
| series | BMJ Open |
| spelling | doaj-art-a70896859c874bc1897535adc3a295bd2025-01-28T07:35:09ZengBMJ Publishing GroupBMJ Open2044-60552022-06-0112610.1136/bmjopen-2021-057977Large-scale evidence generation and evaluation across a network of databases for type 2 diabetes mellitus (LEGEND-T2DM): a protocol for a series of multinational, real-world comparative cardiovascular effectiveness and safety studiesRohan Khera0Yuan Lu1Harlan M Krumholz2Martijn J Schuemie3Ruijun Chen4George Hripcsak5Marc A Suchard6Patrick B Ryan7Anna Ostropolets8Section of Health Informatics, Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut, USACenter for New Drug Safety Evaluation and Research, State Key Laboratory of Natural Medicines, China Pharmaceutical University, Nanjing, ChinaRobert Wood Johnson Foundation Clinical Scholars Program, Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut, USAEpidemiology, Office of the Chief Medical Officer, Johnson & Johnson, Titusville, NJ, USABiomedical Informatics, Columbia University Irving Medical Center, New York City, New York, USADepartment of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, USADepartment of Biostatistics, Fielding School of Publich Health, University of California, Los Angeles, California, USADepartment of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, USADepartment of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, USAIntroduction Therapeutic options for type 2 diabetes mellitus (T2DM) have expanded over the last decade with the emergence of cardioprotective novel agents, but without such data for older drugs, leaving a critical gap in our understanding of the relative effects of T2DM agents on cardiovascular risk.Methods and analysis The large-scale evidence generations across a network of databases for T2DM (LEGEND-T2DM) initiative is a series of systematic, large-scale, multinational, real-world comparative cardiovascular effectiveness and safety studies of all four major second-line anti-hyperglycaemic agents, including sodium–glucose co-transporter-2 inhibitor, glucagon-like peptide-1 receptor agonist, dipeptidyl peptidase-4 inhibitor and sulfonylureas. LEGEND-T2DM will leverage the Observational Health Data Sciences and Informatics (OHDSI) community that provides access to a global network of administrative claims and electronic health record data sources, representing 190 million patients in the USA and about 50 million internationally. LEGEND-T2DM will identify all adult, patients with T2DM who newly initiate a traditionally second-line T2DM agent. Using an active comparator, new-user cohort design, LEGEND-T2DM will execute all pairwise class-versus-class and drug-versus-drug comparisons in each data source, producing extensive study diagnostics that assess reliability and generalisability through cohort balance and equipoise to examine the relative risk of cardiovascular and safety outcomes. The primary cardiovascular outcomes include a composite of major adverse cardiovascular events and a series of safety outcomes. The study will pursue data-driven, large-scale propensity adjustment for measured confounding, a large set of negative control outcome experiments to address unmeasured and systematic bias.Ethics and dissemination The study ensures data safety through a federated analytic approach and follows research best practices, including prespecification and full disclosure of results. LEGEND-T2DM is dedicated to open science and transparency and will publicly share all analytic code from reproducible cohort definitions through turn-key software, enabling other research groups to leverage our methods, data and results to verify and extend our findings.https://bmjopen.bmj.com/content/12/6/e057977.full |
| spellingShingle | Rohan Khera Yuan Lu Harlan M Krumholz Martijn J Schuemie Ruijun Chen George Hripcsak Marc A Suchard Patrick B Ryan Anna Ostropolets Large-scale evidence generation and evaluation across a network of databases for type 2 diabetes mellitus (LEGEND-T2DM): a protocol for a series of multinational, real-world comparative cardiovascular effectiveness and safety studies BMJ Open |
| title | Large-scale evidence generation and evaluation across a network of databases for type 2 diabetes mellitus (LEGEND-T2DM): a protocol for a series of multinational, real-world comparative cardiovascular effectiveness and safety studies |
| title_full | Large-scale evidence generation and evaluation across a network of databases for type 2 diabetes mellitus (LEGEND-T2DM): a protocol for a series of multinational, real-world comparative cardiovascular effectiveness and safety studies |
| title_fullStr | Large-scale evidence generation and evaluation across a network of databases for type 2 diabetes mellitus (LEGEND-T2DM): a protocol for a series of multinational, real-world comparative cardiovascular effectiveness and safety studies |
| title_full_unstemmed | Large-scale evidence generation and evaluation across a network of databases for type 2 diabetes mellitus (LEGEND-T2DM): a protocol for a series of multinational, real-world comparative cardiovascular effectiveness and safety studies |
| title_short | Large-scale evidence generation and evaluation across a network of databases for type 2 diabetes mellitus (LEGEND-T2DM): a protocol for a series of multinational, real-world comparative cardiovascular effectiveness and safety studies |
| title_sort | large scale evidence generation and evaluation across a network of databases for type 2 diabetes mellitus legend t2dm a protocol for a series of multinational real world comparative cardiovascular effectiveness and safety studies |
| url | https://bmjopen.bmj.com/content/12/6/e057977.full |
| work_keys_str_mv | AT rohankhera largescaleevidencegenerationandevaluationacrossanetworkofdatabasesfortype2diabetesmellituslegendt2dmaprotocolforaseriesofmultinationalrealworldcomparativecardiovasculareffectivenessandsafetystudies AT yuanlu largescaleevidencegenerationandevaluationacrossanetworkofdatabasesfortype2diabetesmellituslegendt2dmaprotocolforaseriesofmultinationalrealworldcomparativecardiovasculareffectivenessandsafetystudies AT harlanmkrumholz largescaleevidencegenerationandevaluationacrossanetworkofdatabasesfortype2diabetesmellituslegendt2dmaprotocolforaseriesofmultinationalrealworldcomparativecardiovasculareffectivenessandsafetystudies AT martijnjschuemie largescaleevidencegenerationandevaluationacrossanetworkofdatabasesfortype2diabetesmellituslegendt2dmaprotocolforaseriesofmultinationalrealworldcomparativecardiovasculareffectivenessandsafetystudies AT ruijunchen largescaleevidencegenerationandevaluationacrossanetworkofdatabasesfortype2diabetesmellituslegendt2dmaprotocolforaseriesofmultinationalrealworldcomparativecardiovasculareffectivenessandsafetystudies AT georgehripcsak largescaleevidencegenerationandevaluationacrossanetworkofdatabasesfortype2diabetesmellituslegendt2dmaprotocolforaseriesofmultinationalrealworldcomparativecardiovasculareffectivenessandsafetystudies AT marcasuchard largescaleevidencegenerationandevaluationacrossanetworkofdatabasesfortype2diabetesmellituslegendt2dmaprotocolforaseriesofmultinationalrealworldcomparativecardiovasculareffectivenessandsafetystudies AT patrickbryan largescaleevidencegenerationandevaluationacrossanetworkofdatabasesfortype2diabetesmellituslegendt2dmaprotocolforaseriesofmultinationalrealworldcomparativecardiovasculareffectivenessandsafetystudies AT annaostropolets largescaleevidencegenerationandevaluationacrossanetworkofdatabasesfortype2diabetesmellituslegendt2dmaprotocolforaseriesofmultinationalrealworldcomparativecardiovasculareffectivenessandsafetystudies |