Principles for the Ethical Analysis of Clinical And Translational Research Jonathan Gelfond, MD PhDElizabeth Heitman, PhD Craig Klugman, PhDVanderbilt University Brad Pollock, MPH PhD UT Health Science Center San Antonio, Texas

Objectives 1 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 Critical in Translational Research 1 Incorrect stats could harm participants 2 Invalid stats may squander resources 3 Poor stats diminish the scientific value of resultant discoveries 4 Poor stats promote public distrust in research 5 Poor analysis could harm the public at large 6 Poor stats harm the reputation of the statistical profession

Parsimonious Model Selection Appropriate Study Design Quantification of Evidence Avoidance of Misinterpretation Verification of Assumptions 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): Ethical Guidelines

Element 1 Acceptance of Professional Ethical Guidelines

Element 2 Multidisciplinary Expertise: Scientific & Statistical

Expertise or not? Range of Knowledge and Skill ◦ Competence ◦ Expertise ◦ Authority ◦ Identity Should non-statisticians perform analyses? ◦ Degree of difficulty & risk

Element 3 Objectivity: The analyses should not be conducted to unduly favor one set of hypotheses.

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

Openness & Transparency Accountability through Documentation Documentation is currently poor ◦ Document the good the bad & the ugly Technically challenging ◦ Need a dataset “electronic health record” ◦ Data Provenance systems Ethically challenging ◦ Privacy for study participants ◦ Confidentiality for researchers

Element 5 Verification of Assumptions (Conditions for Validity)

Element 6 Accuracy of the primary data and computation

Accuracy No Fabrication of Data Errors by neglect are unethical Substandard practices promote errors ◦ Use of Excel ◦ Use of point-and-click software ◦ Published stats & p-values are often wrong Reproducibility in Statistics

Element 7 Appropriate Experimental Design and Sample Size

Appropriate Design & Sample Size Poorly 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 8 Parsimonious Model Selection Weighed Against Precision, Bias, and Validity

Trade-offs in Model Selection Parsimony Variance Bias Fit with Data

Element 9 Interpretable quantification of evidence

Element 10 Avoidance of misinterpretation

Avoidance of Misinterpretation Claims not supported by evidence are harmful Interpretation of statistical results occurs after analyses by ◦ Authors ◦ Editors ◦ Journalists ◦ Practitioners

Avoidance of Misinterpretation Boutron et al JAMA(2010) ◦ Study of Negative Randomized Trials ◦ Use of spin in 40% these trials!