Principles for the Ethical Principles for the Ethical Analysis ofAnalysis ofClinical And Translational Clinical And Translational ResearchResearch

Jonathan Gelfond, MD PhD Elizabeth Heitman, PhDCraig Klugman, PhD Vanderbilt UniversityBrad Pollock, MPH PhDUT Health Science CenterSan Antonio, Texas

ObjectivesObjectives1 Research ethics includes more than

just human subject issues.2 Statistical standards in clinical

translational research are an ethical responsibility.

3 Good statistical practice goes beyond avoiding intentional misrepresentation.

4 Define the basic elements and principles of good statistical practice.

Statistical Analysis CriticalStatistical Analysis Criticalin Translational Researchin Translational Research

1 Incorrect stats could harm participants2 Invalid stats may squander resources3 Poor stats diminish the scientific value

of resultant discoveries4 Poor stats promote public distrust in

research5 Poor analysis could harm the public at

large6 Poor stats harm the reputation of the

statistical profession

Parsimonious Model Selection

AppropriateStudy Design

Quantification of Evidence

Avoidance ofMisinterpretation

Verification ofAssumptions

Research Timeline

Objectivity

Multidisciplinary Expertise (Statistical & Scientific)

Openness & Transparency

Accuracy of Data & Computation

Gelfond J, Heitman E, Pollock B, Klugman C: Principles for the Ethical Analysis of Clinical and Translational Research. Statistics in Medicine 2011, 30(23):2785-2792.

Ethical Guidelines

Element 1 Element 1

Acceptance of Professional Ethical Guidelines

Element 2Element 2Multidisciplinary Expertise:Scientific & Statistical

Expertise or not?Expertise or not?Range of Knowledge and Skill

◦Competence◦Expertise◦Authority◦Identity

Should non-statisticians perform analyses?◦Degree of difficulty & risk

Element 3Element 3

Objectivity:The analyses should not be conducted to unduly favor one set of hypotheses.

Element 4Element 4Openness and Transparency:All relevant data and analyses must be presented.

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Openness & TransparencyOpenness & TransparencyAccountability through

DocumentationDocumentation is currently poor

◦Document the good the bad & the uglyTechnically challenging

◦Need a dataset “electronic health record”◦Data Provenance systems

Ethically challenging◦Privacy for study participants◦Confidentiality for researchers

Element 5Element 5Verification ofAssumptions(Conditions for Validity)

Element 6Element 6Accuracy of the primary data and computation

AccuracyAccuracyNo Fabrication of DataErrors by neglect are unethicalSubstandard practices promote

errors◦Use of Excel◦Use of point-and-click software◦Published stats & p-values are often

wrongReproducibility in Statistics

Element 7Element 7

Appropriate Experimental Design and Sample Size

Appropriate Design &Appropriate Design &Sample SizeSample SizePoorly designed experiments have

little positive value and are seldom recoverable by statistical analyses

Too few patients are noninformative

Too many patients may cause excessive harm

Element 8Element 8Parsimonious Model SelectionWeighed AgainstPrecision, Bias, and Validity

Trade-offs in Model Trade-offs in Model SelectionSelection

Parsimony

VarianceBias

Fit with Data

Element 9Element 9Interpretable quantification of evidence

Element 10Element 10

Avoidance of misinterpretation

Avoidance of Avoidance of MisinterpretationMisinterpretationClaims not supported by

evidence are harmful

Interpretation of statistical results occurs after analyses by◦Authors◦Editors◦Journalists◦Practitioners

Avoidance of Avoidance of MisinterpretationMisinterpretationBoutron et al JAMA(2010)

◦Study of Negative Randomized Trials

◦Use of spin in 40% these trials!