FDA Industry Workshop Statistics in the FDA & Industry The Future

1. FDA Industry WorkshopStatistics in the FDA & IndustryThe Future David L DeMets, PhD Department of Biostatistics & Medical Informatics University of Wisconsin School of Medicine & Public Health

2. Topics • Training/Certification Needs • Academic/Industry Collaborations • Attack on Clinical Trials & Statistics • CT Costs & Data Management • Statistical Methodology Issues

3. Globalization of Clinical Trials • Rate of discovery increasing • Translational into practice is not fully realized • Screening • Prevention • Treatment • Declining Recruitment in US • More trials becoming multinational

4. NIH Roadmap: Discipline of Clinical Research Clinician Clinical Trialist Common Core Knowledge Statistician Clinical Pharm Behavioral Scientist

5. Clinical Research Training: a multidisciplinary workforce • In USA, number of clinical researchers is not increasing • Previous training “on the job”, sort of “trial and error” approach • Rigorous training programs in USA are just starting – NIH Roadmap Initiative • Many disciplines now involved in clinical research without formal training in this science • Threat of the “silver tsunami” • 40% of Clinical Researchers in USA over age 50 • World wide training challenges

6. Training Pyramid in Patient-Oriented Research PhD MS Degree Certificate Degree Workshops

7. Biostatistician Crises • Increasing demand for statistician/biostatisticians in academia, industry & government • Supply of MS and especially PhD trained biostatisticians relatively constant over past two decades • Domestic students in biostatistics in very short supply • Crises not fully appreciated

8. Academic – Industry CT Partnerships • Industry CT funding levels similar to NIH • Need to continue developing relationships • Can be a win-win for all Phases I, II & III • Four key elements • Independent Steering Committee • Independent Statistical Center • Independent Data Monitoring Committee • Freedom to publish • Journals beginning to require investigator independence

9. A Clinical Trial Model Steering Committee Sponsor Regulatory Agencies Independent Data Monitoring Committee (IDMC) Statistical Analysis Center (SAC) Data Management Center (DMC) Central Units (Labs, …) Clinical Centers Institutional Review Board Patients

10. Challenge: Attack on Clinical Trials & Statistics • Pending Congressional Legislation • Wall Street & WSJ • Some Patient Advocacy Groups

11. Senate Bill 1956 • A proposed amendment to Federal Food, Drug & Cosmetic Act • Known as the ACCESS Ammendment • A three tiered approval system • More responsive to “the needs of seriously ill patients”

12. Proposed Three Tier Approval • Tier I • Based on Phase I information • Based on clinical, not statistical analysis • May require post approval studies • Tier II • Based on surrogates or biomarkers • Tier III • Traditional requirements

13. Some Issues in Proposed Legislation • Challenge of placebo controlled studies • De-emphasize statistical analysis-no disapprovals solely on the basis of statistical analysis or 95% CIs • Evidence may be based on uncontrolled studies such as case histories, observational studies, mechanism of actions, computer models… • Outcome data may be a surrogate or biological marker

14. CT Statistical Methodology Issues • Surrogate Outcomes • Composite Outcomes • Non-inferiority Designs • Adaptive Designs • Gene Transfer Designs • Safety Monitoring

15. Surrogate Response Variables • Used as a substitute for Clinical Endpoint • May lead to smaller or shorter studies • Requirements (Prentice, 1989) T = True clinical endpoint S = Surrogate Z = Treatment • Sufficient Conditions 1. S is informative about T (predictive) 2. S fully captures effect of Z on T • Concern: • Correlation is not Causation • Pathways often more complex • Other side effects not seen

16. Failures of Potential Surrogates • Nocturnal Oxygen Therapy Trial (NOTT) • 24 vs 12 hour oxygen in COPD patients • Pulmonary Function tests (NS) • Survival (p<0.001) • CAST • Patients with cardiac arrhythmias • Arrhythmias suppressed • Terminated with increased mortality • Ref (Fleming & DeMets, Annals Intern Med, 1996)

17. Failures of Potential Surrogates • Inotropic Drugs in Heart Failure • Improved heart function but increased mortality • PROMISE, PROFILE, VEST,…. • Lipid lowering but no survival benefit • Women’s Health Initiative & HRT • Increased risk of clotting (PE, DVTs) • Ref (Fleming & DeMets, Annals Intern Med, 1996)

18. Composite Endpoint Rationale • Defined as having occurred if any one of several components is observed • e.g. death, MI, stroke, change in severity,….. • May reduce Sample Size by increasing event rates • Assumes each component sensitive to intervention • Otherwise, power can be lost • May avoid competing risk problem • Death is a competing risk to all other morbid events, probably not independent

19. Problems with Composite Outcomes • Interpretability if individual components go in different directions • e.g. WHI global index– • Death: similar • Fractures: positive • DVTs, PEs: negative • Relevance of a mixed set of components • Trials are adding softer outcomes • Could have a loss of power if some components not responsive • Failure to ascertain components

20. Non-Inferiority Designs • Design to compare a new intervention with an accepted/proven standard • “As good as” with respect to a primary • Has some other advantage (cost, less toxic, less invasive,…..) • Must define a degree of non-inferiority or indifference, δ • Choice is somewhat arbitrary • Absolute or relative scale

21. Difference in Events Test – Standard Drug (Antman et al)

22. Non-Inferiority Methodology • Comparison: New Treatment vs. Standard: RRa Upper CI must be less than δ • Estimate of standard vs. placebo: RRb Based on literature • Imputed effect of New Trt vs. placebo (RRc) RRc = RRa x RRb

23. Challenges for Non-Inferiority Designs • Current paradigm makes all non-inferiority trials vulnerable • Relevance of standard vs placebo historical estimate • Fraction of standard benefit to be retained • Choice of δ for current trial

24. Adaptive Designs • Many Adaptive Designs in Use • Baseline Driven (based on risk profile) • Total Event Driven Designs • Group Sequential Designs • Benefit or Harm • Futility • Drop the Losing Arm • Statistical & Logistical issues worked out for these • Not a Frequentist vs Bayesian Issue

25. Adaptive Designs • Adjusting design during trial • Sample size • Primary outcome • Current interest very high • A need exists to be adaptive or flexible • Some statistical methods developed • Still many statistical debates • Many remaining issues related to logistics & potential for introducing bias

26. Monitoring of Clinical Trials • Shalala • Death of gene transfer patient • NEJM (2000) • Press Release (2000) • IRBs often not provided sufficient information to evaluate clinical trials fully • NIH will require monitoring plans for Phase I, II and III trials - guidelines • FDA issued guidelines for Data & Safety Monitoring Boards and IRBs (2001, 2005) • Post Cox II issues • Rapid access vs long term safety

27. IRB Safety Monitoring Problem • IRBs review trial design and ethics • IRBs responsible for patient safety • Drowning in SAE reports, not useful • Inadequate infrastructure to be able to provide adequate safety monitoring • For some multicenter trials, an alternative process exists (i.e. DMC) • For single center trials, patient “safety” monitoring provided is now inadequate

28. Safety & Observational Data • Long term RCT follow-up for low rate SAEs not common • Have turned to observational data as a supplement • Serious limitations to argue causality due to confounding and bias • Statistical analysis can take us only so far • Need to understand better what can be learned

29. Reducing Trial Costs • DCRI Workshop: Hypothetical Trial Example • 60-70% of cost site related, half due to site monitoring • Could reduce costs 40% by reducing CRFs & monitoring site visits • DCRI CT example: Ongoing site monitoring improved regulatory compliance but little on trial data results & conclusions • Breast Cancer Fraud Case – Academic network; Intense audit did not alter the results (<1% error), NEJM 1995

30. Need for Change in Site Monitoring • Current system is “out of control” • Educate/train clinical sites & investigators • Focus data collected & limit the extraneous • Set priorities on monitoring key variables: • eligibility • primary and secondary outcomes, • serious adverse events (SAE) • Sample audit the rest • Use more statistical QC methods • Standardize CRFs and data management

31. Challenge: Gene Transfer Trials • NIH Re-Combinant Advisory Committee (RAC) • RAC reviews new gene transfer trials • Mostly very early phase studies • Designs often not appropriate • No objectives clearly stated • Borrowed from other settings that are not relevant • Design guidelines need further development

32. Summary • With current discovery rate, future appears very promising • Significant challenges exist • Most are solvable but will require collaboration from academia, regulators & sponsors • Failure is not an option – we need evidence based medicine • Every challenge is an opportunity