NDLI: Assessing the Integrity of Clinical Data: When is Statistical Evidence Too Good to be True?

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Topoi : Volume 36

Topoi : Volume 35

Topoi : Volume 34

Topoi : Volume 33

Topoi : Volume 33, Issue 2, October 2014

Introduction: Evidence and Causality in the Sciences

Hunting Side Effects and Explaining Them: Should We Reverse Evidence Hierarchies Upside Down?

Down with the Hierarchies

Assessing the Integrity of Clinical Data: When is Statistical Evidence Too Good to be True?

Mechanisms and the Evidence Hierarchy

Process Tracing as an Effective Epistemic Complement

Assessing Direct and Indirect Evidence in Linguistic Research

Causation in Personal Injury Law: The Case for a Probabilistic Approach

The Contextual Character of Causal Evidence

Physics and the Human Face of Causation

The Structure of Causal Evidence Based on Eliminative Induction

Introduction: Psychology and Culture

Infants Understand How Testimony Works

Wired for Society: Cognizing Pathways to Society and Culture

History and Intentions in the Experience of Artworks

Cognitive Science of Religion and the Study of Theological Concepts

The Virtues of Ingenuity: Reasoning and Arguing without Bias

Scientists’ Argumentative Reasoning

Causal Exclusion and Multiple Realizations

Rules as the Impetus of Cultural Evolution

Untimely Reviews ( Topoi , Volume 33 , Issue 2 )

Friedrich Nietzsche’s Twilight of the Idols

Topoi : Volume 33, Issue 1, April 2014

Assessing the Integrity of Clinical Data: When is Statistical Evidence Too Good to be True?

Content Provider	Springer Nature Link
Author	MacDougall, Margaret
Copyright Year	2014
Abstract	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.
Starting Page	323
Ending Page	337
Page Count	15
File Format	PDF
ISSN	01677411
Journal	Topoi
Volume Number	33
Issue Number	2
e-ISSN	15728749
Language	English
Publisher	Springer Netherlands
Publisher Date	2014-02-16
Publisher Place	Dordrecht
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Baseline data Benford’s law Cluster analysis Mahalanobis distance Scientific fraud Statistical evidence Philosophy Philosophy of Science Philosophy of Technology
Content Type	Text
Resource Type	Article
Subject	Philosophy

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Assessing the Integrity of Clinical Data: When is Statistical Evidence Too Good to be True?

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