Principles of Large-scale Evidence Generation and Evaluation across a Network of Databases (LEGEND).
Evidence derived from existing health-care data, such as administrative claims and electronic health records, can fill evidence gaps in medicine. However, many claim such data cannot be used to estimate causal treatment effects because of the potential for observational study bias; for example, due...
| Publicado en: | Journal of the American Medical Informatics Association Vol. 27; no. 8; pp. 1331 - 1338 |
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| Autores principales: | , , , , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Oxford University Press / USA
Aug2020
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=145734101&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 145734101 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 10675027 FZ9 jtl: Journal of the American Medical Informatics Association issn: 10675027 maglogo: N pubinfo: dt: Aug2020 vid: 27 iid: 8 pid: 622 pub: Oxford University Press / USA artinfo: ui: 145734101 145734101 NLM32909033 145734101 10.1093/jamia/ocaa103 NLM32909033 145734101 ppf: 1331 ppct: 7 formats: tig: atl: Principles of Large-scale Evidence Generation and Evaluation across a Network of Databases (LEGEND). aug: au: Schuemie, Martijn J Ryan, Patrick B Pratt, Nicole Chen, RuiJun You, Seng Chan Krumholz, Harlan M Madigan, David Hripcsak, George Suchard, Marc A affil: Epidemiology Analytics , Janssen Research and Development, Titusville, New Jersey, USA sug: subj: Meta Analysis Computer Communication Networks Hypertension Drug Therapy Resource Databases Antihypertensive Agents Therapeutic Use Antihypertensive Agents Adverse Effects Probability Data Analysis, Statistical Observational Methods Treatment Outcomes Clinical Trials Human Confidence Intervals Comparative Studies Multicenter Studies Evaluation Research Validation Studies ab: Evidence derived from existing health-care data, such as administrative claims and electronic health records, can fill evidence gaps in medicine. However, many claim such data cannot be used to estimate causal treatment effects because of the potential for observational study bias; for example, due to residual confounding. Other concerns include P hacking and publication bias. In response, the Observational Health Data Sciences and Informatics international collaborative launched the Large-scale Evidence Generation and Evaluation across a Network of Databases (LEGEND) research initiative. Its mission is to generate evidence on the effects of medical interventions using observational health-care databases while addressing the aforementioned concerns by following a recently proposed paradigm. We define 10 principles of LEGEND that enshrine this new paradigm, prescribing the generation and dissemination of evidence on many research questions at once; for example, comparing all treatments for a disease for many outcomes, thus preventing publication bias. These questions are answered using a prespecified and systematic approach, avoiding P hacking. Best-practice statistical methods address measured confounding, and control questions (research questions where the answer is known) quantify potential residual bias. Finally, the evidence is generated in a network of databases to assess consistency by sharing open-source analytics code to enhance transparency and reproducibility, but without sharing patient-level information. Here we detail the LEGEND principles and provide a generic overview of a LEGEND study. Our companion paper highlights an example study on the effects of hypertension treatments, and evaluates the internal and external validity of the evidence we generate. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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