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A Regulatory Perspective on Threats to the Integrity of
Analgesic Clinical Trial Efficacy DataSharon Hertz, MDDivision Director
Division of Anesthesia, Analgesia, and Addiction Products
FDA/CDER
Disclaimer
The content of this talk does not necessarily reflect the views of the FDA, and is entirely based on my own observations and viewpoints.
I have no potential conflicts of interest to report.
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TIACTED
Errors in the design, the conduct, the data collection process, and the analysis of a randomized trial have the potential to affect not only the safety of the patients in the trial, but also, through the introduction of bias, the safety of future patients.*
* Colin Baigent, Frank E Harrell, Marc Buyse, Jonathan R Emberson and Douglas G Altman, Ensuring trial validity by data quality assurance and diversification of monitoring methods, Clinical Trials 2008; 5: 49–55
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TIACTED
Beyond the potential to affect safety, threats to clinical trial data integrity affect the ability to demonstrate efficacy and can substantially increase the time to get new products to market.
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What Are TIACTED?• Inadequate study design• Sloppy study conduct
–Poor training/supervision of clinical site staff, patients
–Protocol violations by clinical site staff, patients
–Unverifiable data/audit trail
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What Are TIACTED?
• Intentional actions that negatively affect clinical trial data integrity– Deceptive subjects– Fraudulent data– Intentional failure to adhere to protocol– Improper handing of data– Deviation from prespecified analyses
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Example 1 - Investigator Fraud?
Study Study 1 Study 2 Study 3
Treatment Study Drug
Placebo Study Drug
Placebo Study Drug
Placebo
PID, VAS 24 hours 72 hours 72 hours
Mean ± SD -46 ± 22 -13. ± 13 -57 ± 16 -20 ± 12 -31 ± 21 -31 ± 21
Difference from placebo
LS Mean (95% C.I.)
-32 (-37, -28) -36 (-40, -32) -0.7 (-5, 3)
p-value <0.0001 <0.0001 0.76
Study Location Non-US Non-US US
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• 3 clinical efficacy trials with very similar design: 2 successful, 1 failed
Example 1
• Successful studies– Large effect size, larger than expected – Higher baseline pain intensity– Less use of rescue, non-drug treatment– Lower placebo response
• Smaller change PI, 0% placebo reported onset of meaningful PR
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Example 1
• What could explain the difference?– Demographics mostly similar– Looked for treatment by site effect – results
not driven by one site– Compared to other similar product trial results
including US and other non-US trials – no other studies with similar placebo response or effect size
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Example 1• Site Inspections of 3 sites common to both non-US
studies, based on high enrollment numbers– 1 site
• Study nurse transcribed PI notes “to be legible”, destroyed original documents
• 21 subjects enrolled in both studies, 14 of whom injured and enrolled on same day, for both studies
• 17 of 55 in study 1, 6 of 35 in study 2 - part of a pair or triplet with same surname and/or address and many with same day of injury
• Site excluded from analysis
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Example 1• Findings from site 1 led to evaluation for similar patterns
of enrollment from other sites– All sites had some same-day enrollment from related
subjects or subjects sharing an address, and multiple subjects enrolled in both studies
– Applicant explained that multiple members of the same family or household could sustain the same injury, on the same day, repeatedly, because people in this country more active than US
– Unable to verify identity of any subjects based on local privacy laws
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Example 2 – Failure to Follow Protocol
• 2 clinical efficacy trials, one single site for both studies
• Inspection findings– Failure to record safety variables, investigator
felt protocol required too frequent recording of vital signs (although research assistant present to record dosing)
– No automated blood pressure machine available for baseline measures
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Example 2
• Additional data requested• Possible safety problems, data insufficient to
adequately characterize safety
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Example 3 – Improper Handling of Data
• 1st review cycle – routine inspections –– Protocol deviations that could impact the
validity, reliability, and integrity of data– Applicant failed to report protocol violations in
final study report– Accidental unblinding at several sites
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Example 3• 2nd review cycle - pivotal study repeated, routine
inspections of 2 clinical sites and applicant– Statisticians extracted data for SAS datasets
with unblinded treatment assignment field prior to database lock
– Variable subsequently blinded– Datasets (not actual data) deleted– Applicant claimed statisticians either did not
view data or had no interaction with sites or the critical outcome data
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Example 3
• Applicant failed to notify FDA when event occurred, even though unblinding contributed to initial CR
• Failed to maintain audit trails for the deletion of datasets
• FDA unable to confirm attestations of statisticians, no longer with company
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End Note• Three examples demonstrated importance
of early identification of data integrity problems, corrections may salvage study
• Better clinical trial monitoring may help identify problems earlier
• Important to notify FDA, may be able to help
• Best approach – avoid these problems
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