Assessing the Integrity of Clinical Data: When is Statistical Evidence Too Good to be True?
Evidence, as viewed through the lens of statistical significance, is not always as it appears! In the investigation of clinical research findings arising from statistical analyses, a fundamental initial step for the emerging fraud detective is to retrieve the source data for cross-examination with t...
| Publicado en: | Topoi: An International Review of Philosophy Vol. 33; no. 2; pp. 323 - 338 |
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| Formato: | Artículo |
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Springer Nature
Oct2014
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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=hlh&AN=97852230&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 97852230 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 01677411 NM2 jtl: Topoi: An International Review of Philosophy issn: 01677411 maglogo: N pubinfo: dt: Oct2014 vid: 33 iid: 2 pid: 237 pub: Springer Nature artinfo: ui: 97852230 10.1007/s11245-013-9216-5 ppf: 323 ppct: 15 formats: fmt: @attributes: type: P size: 851KB tig: atl: Assessing the Integrity of Clinical Data: When is Statistical Evidence Too Good to be True? aug: au: MacDougall, Margaret affil: Medical Statistician and Researcher in Education, Centre for Population Health Sciences, College of Medicine and Veterinary Medicine, University of Edinburgh, Edinburgh UK su: Science & ethics Benford's law (Statistics) Cluster analysis (Statistics) Statistical reliability Fraud in science Falsification of data sug: subj: Science & ethics Benford's law (Statistics) Cluster analysis (Statistics) Statistical reliability Fraud in science Falsification of data keyword: Baseline data Benford's law Cluster analysis Mahalanobis distance Scientific fraud Statistical evidence ab: Evidence, as viewed through the lens of statistical significance, is not always as it appears! In the investigation of clinical research findings arising from statistical analyses, a fundamental initial step for the emerging fraud detective is to retrieve the source data for cross-examination with the study data. Recognizing that source data are not always forthcoming and that, realistically speaking, the investigator may be uninitiated in fraud detection and investigation, this paper will highlight some key methodological procedures for providing a sounder evidence base for withdrawing from a study on grounds of integrity. The promotion of patient safety is paramount. However, there is a broader rationale for disseminating these ideas. This includes empowering researchers to optimize their personal integrity, make informed choices regarding membership of future research collaborations and successfully voice their concerns to journal editors, particularly where a conflict of interests can render such dialogues particularly difficult. Recommendations will be supported by topical case studies and practical steps involving data exploration, testing of baseline data and application of Benford's Law. While this paper has a clinical focus, the advice provided is transferrable to a wide range of multidisciplinary research settings outside of Medicine. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y custom: Topoi: An International Review of Philosophy is a copyright of Springer, 2014. All Rights Reserved. item: Topoi: An International Review of Philosophy holder: Springer Nature dt: @attributes: year: 2014 holdings: @attributes: islocal: N |
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