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Data Mining AERS FDA’s (Spontaneous) Adverse Event Reporting System Division of Drug Risk Evaluation Office of Drug Safe

Data Mining AERS FDA’s (Spontaneous) Adverse Event Reporting System Division of Drug Risk Evaluation Office of Drug Safety. Carolyn McCloskey, M.D., M.P.H. Drug Safety and Risk Management Advisory Committee May 18, 2005. Outline. Brief History of data mining (DM) activities at the FDA

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Data Mining AERS FDA’s (Spontaneous) Adverse Event Reporting System Division of Drug Risk Evaluation Office of Drug Safe

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  1. Data Mining AERSFDA’s (Spontaneous) Adverse Event Reporting SystemDivision of Drug Risk EvaluationOffice of Drug Safety Carolyn McCloskey, M.D., M.P.H. Drug Safety and Risk Management Advisory Committee May 18, 2005

  2. Outline • Brief History of data mining (DM) activities at the FDA • Current Use in the Division of Drug Risk Evaluation (DDRE) – CRADA • Application Development - WebVDME • Pilot - Examples and Selected Conclusions • Other CRADA Activities • Future Directions in DDRE Pharmacovigilance

  3. Historical Overview

  4. CRADA - Cooperative Research and Development Agreement WebVisual Data Mining Environment (WebVDME) With Lincoln Technologies, Inc. March 2003 – July 2005

  5. CRADA Objectives • User-friendly application • Web-based environment • Performance Evaluations by User Groups • Training • Continued development and refinement

  6. Empirical Bayes Geometric Mean (EBGM) • An observed/expected score • Adjusts for sampling variation (e.g., sample size) • No adjustment for reporting bias • Allows data stratification in DM software • Standard stratification: gender, age group, year

  7. EB05 – EB95 Interval • Interval in which the EBGM score could be found because of sampling variability • EB05 is the lower bound • EB95 is the upper bound • 90% probability of EBGM occurring between EB05 and EB95

  8. Example of Sampling Variability Adjustment (for small numbers)

  9. CRADA Pre-PilotPerformance EvaluationsMay 2003 – October 2003 • WebVDME record retrieval validation with AERS case retrieval • Multiple trade & ingredient nomenclature • Drug assignment allocations (suspect & concomitant) • Duplicate removal logic • OIT system performance evaluations

  10. CRADA PilotMedical Safety Evaluators • Evaluated data mining scores for drugs and biologics • Indication vs. new signal • Labeled vs. unlabeled • Innocent bystanders or concomitant medications • Drug names • Safety Evaluators’ ease of use

  11. CRADA PilotEpidemiologists • Evaluated • Temporal trends • Drug name selections • Suspect & Concomitant selections • Stratification • Signal strengths • Epidemiologists’ ease of use

  12. Pilot Examples • New vs. Old Drug • EBGM Rankings & Confidence Intervals

  13. New Drug (1 Year)EBGM (EB05-EB95) EB05 =2.0

  14. OLDER DRUG (>10 Years)EBGM (EB05-EB95) EB05 =2.0

  15. Selected Pilot Conclusions - 1 • WebVDME DM - Statistical tool assistsin identifyingunusual patterns with AERS data but • ! Patterns Need Interpretation!

  16. Selected Pilot Conclusions - 2 • Knowledge of data in database imperative to interpret • Clinical & pharmacologic activities of drug • Other - reporting disproportionalities which also reflect limitations of AERS data

  17. AERS DATABASE LIMITATIONS

  18. Continuing CRADA Activities - DDREMarch 2004 - Present • Access by interested Reviewers to WebVDME • Training • Application • Refinements addressing • Technical problems identified • Customization by user needs

  19. Summary – 1 DM Signals in DDRE • Assist in prioritizing evaluations of case series • Identify associations, NOT a cause or degree of risk • Reflect limitations of data

  20. Summary – 2 DM Signals in DDRE • Threshold a compromise between sensitivity and specificity (false positives & negatives) • Absence of a DM signal≠ absence of a drug-event association • Magnitude of DM scores≠ magnitude of risk • Always require clinical case reportand reporting bias evaluation

  21. Future Directions of DM • Prospective signal detection • Parallel use with traditional pharmacovigilance methods in DDRE • Continued research in more advanced methodology (Drug-drug interaction & logistic regression modeling)

  22. Acknowledgments • Division of Drug Risk Evaluation • Rita Ouellet-Hellstrom, Ph.D., M.P.H. • Mary Willy, Ph.D. • Mark Avigan, M.D., C.M.

  23. THANK YOU

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