STATISTICS 542 Introduction to Clinical Trials

1. STATISTICS 542Introduction to Clinical Trials David L. DeMets, Ph.D. Dept. of Biostatistics & Medical Informatics 600 Highland Avenue Room K6/446 Phone: 263-1706 E-Mail: demets@biostat.wisc.edu

2. STATISTICS 542 - REFERENCES 1. Friedman, Furberg & DeMets (3rd edition, 1998) Fundamentals of Clinical Trials. Springer-Verlag, NY, NY. 2. Pocock (1986) Clinical Trials - A Practical Approach. Wiley and Sons, New York, NY. 3. Meinert (1986) Clinical Trials - Design, Conduct & Analysis. Oxford University Press, New York, NY. 4. Hill (1962) Statistical Methods of Clinical and Preventive Medicine. Oxford University Press, New York, NY. 5. Tygstrup, Lachin & Juhl (1982) The Randomized Clinical Trial and Therapeutics Decisions. Marcell Dekker, New York, NY. 6. Shapiro & Louis (1983) Clinical Trials - Issues and Approaches. Marcell Dekker, New York, NY. 7. Mike & Stanley (1982) Statistics in Medicine Research. Wiley and Sons, New York. 8. Bulpitt (1983) Randomized Controlled Clinical Trials. Martinus Nijhoff, Boston, MA. 9. Peto, Pike, Armitage, et al. (1976) Design and Analysis of Randomized Clinical Trials Requiring Prolonged Observation of Each Patient. British Journal of Cancer.

3. Rationale for Biostat 542 • Common goal; critical review of the literature throughout our careers • Clinical practice • Clinical research • Special goal: to conduct clinical research, some understanding of study design & analysis is necessary

4. Clinical Practice & the Literature • Not all published articles of the same quality, regardless of journal • Not all evidence of the same level • Anecdotal • Observational • Experimental • Practicioner needs to sort out the evidence

5. Clinical Research • Many challenges • Clinical research workforce • Adequate training programs • Funding • Political & financial pressures • Practice vs research time conflicts • NIH initiatives

6. NIH Road MapZerhouni (2003) Science • New Pathways to Discovery • Research Teams of the Future • Re-engineering the Clinical Research Enterprise • Translational Research • Clinical Research Networks • Clinical Research Workforce Training

7. Discipline of Clinical Research Clinician Clinical Trialist Common Core Knowledge Statistician Clinical Pharm Behavioral Scientist

8. Clinical Research Training: a multidisciplinary workforce • Previous training “on the job”, sort of “trial and error” • Rigorous training programs are just starting • Many disciplines now involved in clinical research without formal training in this science • Threat of the “silver tsunami”

9. Composition of the Physician Workforce in the U.S. - 1980-2003 Ref: JAMA 2005; 294(11):1343-51

10. Aging of Funded NIH Investigators 1985-2004 Ref: JAMA 2005; 294(11):1343-51

11. Multidisciplinary Clinical Research Career Development (K12) NIH Clinical Research Career Development Programs College Professional Post-Doc Prof. Advancement Grad School Pre-doctoral Clinical Research Training Program (T32)

12. Training Pyramid in Patient-Oriented Research PhD MS Degree Capstone Degree Workshops

13. NIH RPG Funding Success Rates: 1985-2000“Similar Success” Ref: JAMA 2005; 294(11):1343-51

14. Application Trends for NIH Grants Ref: JAMA 2005; 294(11):1343-51

15. Physician Researchers • Have similar success rates in obtaining funding as PhD’s • Do not submit as many applications • Have not taken advantage of recent NIH budget increase

16. Clinical Research • Clinic is a perfectly good laboratory • Clinicians busy in their practice can also participate / lead research efforts • Difficult to be in the clinic mostly & run a competitive wet lab

17. NIH Initiative Clinical Translational Science Award (CTSA) • NIH grant investment in 60 research institutions • Focus on three areas • Training • Clinical research infrastructure • Community outreach • UW has submitted its application

18. Wisconsin Network for Health Research (WiNHR) Howard Bailey MD, Dave DeMets Phd, on behalf ofWiNHR InvestigatorsSept, 2005

19. WiNHR - Basic Idea • Create an efficient organizational network of selected clinical sites statewide who would agree to participate in clinical trials or other human subjects based research • Provide research staff support (infrastructure) at each site to provide quality control • Centralize some aspects of the research, e.g. create a data center at the UW-Madison or central IRB • Trial protocols could be generated by any participating site, with review for scientific merit • Individual protocols funded separately, taking advantage of existing infrastructure

20. Rationale • National recognition (e.g. NIH, FDA) of need for more and better evidence based medicine • Each sites local population isn’t large enough or diverse enough to perform efficient/representative research • Public forums expressed the following – • Promote expanded community-based clinical research trials • Build collaborations between community- and UW-based researchers

21. Benefits of WiNHR • Provides population statewide access to clinical trials studying new diagnostic, preventative and therapeutic interventions • Provides statewide practitioners, especially those not involved with larger clinic or med school-based research groups, an opportunity to participate and have more rapid access to advancements in health care • Expands capabilities of Wisconsin researchers focused on human subjects research • More studies and done faster • Possible statewide clinical database of > 3 million lives which would provide a powerful research tool • Improve funding opportunities

22. WiNHR Nodal Sites

23. Wausau Madison Green Bay LaCrosse Milwaukee Janesville Freeport, IL Waukesha Potential WiNHRCollaborators Wisconsin Oncology Network (WON) Sites • • • • •

24. WiNHR Long Range Benefits • Efficiency • A true statewide collaborative health care effort • Well-positioned to compete for health care research • Currently not feasible in single sites, e.g. 100-1,000 subjects per study • In the new post-genomics/informatics era.

25. Wisconsin Oncology Network (WON) • Proof of Concept / UWCCC • 7 sites with collaborating oncologists • 15-20 protocols completed or underway • Patients being accrued, interventions being given • Dr Ann Traynor, WON Director • Largely volunteer basis – can’t expand

26. Wausau Madison Green Bay LaCrosse Milwaukee Janesville Freeport, IL Waukesha Wisconsin Oncology Network

27. Research Cycle Clinical Trials Research (human, comparative) Translational Research Basic Research (bench, animal) Observational Research (human, epidemiological)

28. Evidence-Based Medicine • Ideally based on data from clinical trials • Need to understand fundamentals of good design and analysis • Not all published data of same quality

29. TYPES OF EVIDENCE (1) • Randomized Clinical Trial (RCT) is gold standard • RCT minimizes bias • Can’t do RCTs for all important questions (time, funding, ethics) • Must make choices on what evidence to use for clinical guidelines

30. TYPES OF EVIDENCE (2) • Need to remember limitations of evidence clinical guidelines based on • Continue to strive to improve evidence • Need to read the literature critically

31. TYPES OF EVIDENCE (3) • Recent history teaches us to be cautious • Not seeking most rigorous evidence has proven to be problematic • Theory and observational studies based evidence have not always led to correct conclusion for important questions

32. Types of Clinical Research 1. Case Report Anecdotal  Problem 2. Observational a. Case-Control/Retrospective (lung cancer) b. Cross Sectional (WESDR) Beaver Dam c. Prospective (Framington) WESDR-II  Associations 3. Drug Development Phase 0, Phase I, and Phase II 4. Experimental a. Historical Controls b. Concurrent (Non-randomized) c. Randomized  “Effect”

33. The Cycle of Clinical Therapeutics Discovery RCTs Guidelines Patient Outcomes Performance Indicators Caregiver Performance Califf R et al JACC 2002

34. Comparison of ESC, ACC/AHA, CCS and HFSA

35. I IIa IIb III Guidelines:Classes of Recommendation Intervention is useful and effective Evidence conflicts/opinions differ but lean towards efficacy Evidence conflicts/opinions differ but lean against efficacy Intervention is not useful/effective and may be harmful

38. Comparison of pharmacology therapyESC, ACC/AHA, CCS and HFSA ESC ACC/AHA CCS HFSA Level Class Level Class Level Class Level Class • ACE inhibitor A I A I A I A I • Beta-blocker A I A I A I A IAngiotensin receptor blocker • a) ACE inhibitor intolerant B I A I A I A Ib) ACE inhibitor treated- - B IIb A I A IIa • to reduce mortality B IIa - - - - - - • to reduce hospitalisationA I - - - - - - McMurray JV, Swedberg K. Eur Heart J Aug 2006

39. Alternatives to Evidence Based Medicine • Eminence-based medicine • Confidence-based medicine • Eloquence-based medicine • Vehemence-based medicine • Providence-based medicine • Diffidence-based medicine • Nervousness-based medicine Isaacs D, Fitzgerald D. Br Med J 1999;319:1618.

40. Alternatives to Evidence Based Medicine(Ref:Marc Pfeffer) • Last-patient-experience based med • Hot-tip-based med • My-resident-told-me based med • Chat-room based-med • Direct-to-consumer advertising based med

41. Challenge: Attack on Clinical Trials • Pending Congressional Legislation • NIH/FDA leadership • Wall Street & WSJ • Patient Advocacy Groups • References • SCT (JCT, 2006) • Jacobsen & Parmet (JAMA, Jan 10, 2006) • Cancer Letter (Aug, 2005)

42. Senate Bill 1956 • An amendment to Federal Food, Drug & Cosmetic Act (Senator Brownback-K) • Known as the ACCESS Act • A three tiered approval system • More responsive to “the needs of seriously ill patients”

43. 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 biomarkers & surrogates • Tier III • Standard requirements

44. 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

45. Washington Leadership • Use modern mathematical tools…efficacy derived from large cohort of patients given same treatment • Use Bayesian statistics to glean strong conclusions from large cohort data, just like economists • Develop chemoprevention based on surrogates and biomarkers • RCTs not the best future approach

46. WSJ Editorials • WSJ against 1962 Food, Drug & Cosmetic Act amendment • Places an unnecessary burden by demanding proof of efficacy • Terminally ill patients should be free to take Phase I tested interventions • An old argument getting renewed attention • Current target is FDA oncology

47. Some Advocacy Groups • Abigail Alliance for Better Access • Oncologists have allowed statisticians to take over • A cult-like belief….that you can’t learn anything…unless you do a clinical trial • In environmental toxicology, when a statistician walks in, scientists cringe …you are doing battle with a functional idiot • Ref: Cancer Letter August 5, 2005

48. Court Action • In 2003, Alliance sued for expanded access • August 2004, District Court dismissed case-no fundamental right to INDs • May, 2006 District Court ruling overturned, “access is a constitutional right” • Case probably headed to Supreme Court • Major implications for • Drug safety • Regulatory Authority • Clinical research & practice

49. Definition of a Clinical Trial A prospective study comparing the effect and value of intervention(s) against a control in human subjects NOTE: • Prospective not retrospective • Intervention/Equipment • preventive -drug • therapeutic -device • diagnostic -procedure • -biologic • Control Group • no intervention -current standard therapy • placebo (Diehl, 1938) -previous standard • Humans not animals • ethics • informed consent

50. Clinical TrialsNatural Experiment • General Lancaster (1600) • East Indian Shipping Co. • 4 ships - Lancaster’s ship fortuitously had lemon juice on board • Lancaster’s ship remained free of scurvy • Natural Experiment, not planned