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1 FDA Industry Workshop Statistics in the FDA & Industry The Future David L DeMets, PhD Department of Biostatistics & Medical Informatics University of.

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Presentation on theme: "1 FDA Industry Workshop Statistics in the FDA & Industry The Future David L DeMets, PhD Department of Biostatistics & Medical Informatics University of."— Presentation transcript:

1 1 FDA Industry Workshop Statistics in the FDA & Industry The Future David L DeMets, PhD Department of Biostatistics & Medical Informatics University of Wisconsin School of Medicine & Public Health

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

3 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 4 Common Core Knowledge Clinical Trialist Clinician Statistician Behavioral Scientist Clinical Pharm NIH Roadmap: Discipline of Clinical Research

5 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 6 Training Pyramid in Patient-Oriented Research PhD MS Degree Certificate Degree Workshops

7 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 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 9 Central Units (Labs, …) Clinical Centers Patients Data Management Center (DMC) Sponsor Institutional Review Board Independent Data Monitoring Committee (IDMC) Steering Committee Statistical Analysis Center (SAC) Regulatory Agencies A Clinical Trial Model

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

11 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 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 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 14 CT Statistical Methodology Issues CT Statistical Methodology Issues Surrogate Outcomes Composite Outcomes Non-inferiority Designs Adaptive Designs Gene Transfer Designs Safety Monitoring

15 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 16 Failures of Potential Surrogates 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 17 Failures of Potential Surrogates Failures of Potential Surrogates Inotropic Drugs in Heart Failure –Improved heart function but increased mortality –PROMISE, PROFILE, VEST,…. Lipid lowering but no survival benefit –Womens Health Initiative & HRT –Increased risk of clotting (PE, DVTs) Ref (Fleming & DeMets, Annals Intern Med, 1996)

18 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 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 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 21 Difference in Events Test – Standard Drug (Antman et al)

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

23 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 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 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 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 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 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 29 Reducing Trial Costs 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 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 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 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

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