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

1. 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

2. 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.

3. 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

4. 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

5. 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.

6. 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.