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Emerging Statistical Issues Opportunities in the Conduct and Monitoring of Clinical Trials. Stacy Lindborg, Ph.D. Sr. Director, Global Statistical Sciences and Advanced Analytics Eli Lilly & Company Panel Remarks April 13, 2011.

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emerging statistical issues opportunities in the conduct and monitoring of clinical trials

Emerging Statistical Issues Opportunities in the Conduct and Monitoring of Clinical Trials

Stacy Lindborg, Ph.D.

Sr. Director, Global Statistical Sciences and Advanced AnalyticsEli Lilly & Company

Panel Remarks

April 13, 2011

emerging statistical opportunities a view from a large pharmaceutical company
Emerging Statistical Opportunities: a view from a large Pharmaceutical company

Need more efficient designs, “business as usual” no longer an option.

pTS (Launch) average for a compound entering the clinic is 16%, was 21.5% in the early 1990’s

  • Clinical Trial Optimization, as we leverage its potential, it creates:
    • Operational hurdles – collection of data in a timely fashion, maintaining blind and allow randomization probabilities to change
    • Technical Advances – including tools for clinical trial simulation of trial design comparison, documentation of proper control of error rates, shift towards better decision making.
    • Industry and regulatory acceptance of innovative trials (e.g., Bayesian ) which enables the required sample size in a trial to decrease and/or collection of more useful information
  • Statistical Debate that is emerging with trials being run by CRO/ARO’s
    • Risk vs. Obligations have a very different view from a PI vs. Industry Response

Goal: get to the right answer faster, cheaper and with greater certainty. Kill ineffective/unsafe drugs sooner and get better information on useful drugs.

emerging statistical opportunities a view from a large pharmaceutical company3
Emerging Statistical Opportunities: a view from a large Pharmaceutical company
  • Placebo Response & Rates of Failed trials
    • We’re observing placebo response in diseases historically immune to Placebo Response (e.g., Schizophrenia)
  • CV Safety trials & tQT studies – we need a more effective way to establish CV safety in new medicines
  • Substantial Evidence
  • Missing Data
  • Multi-Regional Trials
general paper remarks
General Paper remarks

Snappin

  • “initially, no multiplicity control existed” …
  • Strong control of Type 1 error rate is what matters – I would advocate that understanding and assessing control of Type 2 error rate is equally as important.

Meaningful Drug Effect

(UnknownTruth)

Positive

Negative

True Positive

False Positive

Positive

Study Conclusion

On Drug Effect

False Negative

True Negative

Negative

Error Rates

general paper remarks5
General Paper remarks

Marc Buyse

  • Introduction of continuum of errors
  • Suggests 1) we need to more systematically focus on Fraud vs. simply errors/sloppiness and 2) we should be more targeted with statistical monitoring of key aspects of trials.

Bryan Shepherd

  • Advocates correcting bias in data by leveraging a sample of data from an audit vs. exhaustive audit
  • approach improved as size of audit sample increased and as error rates & magnitudes were smaller.

Overarching comment on Marc and Bryan’s papers: there is value to considering the role that statistics can play in this arena more broadly.

general paper remarks6
General Paper remarks

Janet Wittes

  • We do not generally operate algorithmically, so the boundary used is a guideline
  • Wisely cautioned us on the method employed to stop early for benefit – not only for p-value to stop, but also calculation of effect size and CI. Recommendation: Stagewise Ordering
  • Protocols need to include monitoring rules and details on calculation of p-values, CI and estimates.