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Multi-regional Clinical Trials Why be concerned ? A Regulatory Perspective on Issues

Multi-regional Clinical Trials Why be concerned ? A Regulatory Perspective on Issues. Robert T. O’Neill Ph.D. Director , Office of Biostatistics Office of Translational Sciences, CDER.

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Multi-regional Clinical Trials Why be concerned ? A Regulatory Perspective on Issues

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  1. Multi-regional Clinical TrialsWhy be concerned ?A Regulatory Perspective on Issues Robert T. O’Neill Ph.D. Director , Office of Biostatistics Office of Translational Sciences, CDER To be presented at the 17th Annual Harvard Schering-Plough Workshop: Global Trials, Challenges and Opportunities ; May 28 and 29, 2009

  2. Outline • Why now - Increasing use of this study design - some FDA experience • What guidance , if any, on the the multi-regional study- its design, analysis, reporting, and interpretation • What are some concerns: quality, training, data collection and management (bio) • Trial is only as good as the investigators: who are they - training of investigators regarding protocol and its compliance • Implementation - time to increase our attention to planning and analysis • Proposal for way forward

  3. Why now • Increasing use of this design in most medical areas • A Summary of two FDA studies on the use and regulatory impact • Increasing experience raises many questions • Validity • Quality • Design • Monitoring • Single large study - relevance • Sparsity of written literature or FDA guidance on the issues that are raising concern • It is time to address them - solutions will not be easy or simple • ICH E5 actually expresses a position on the design • Increasing emphasis on quality of clinical trials, cost and streamlining - Critical Path

  4. This is not just a regulatory or drug development concern

  5. Modernize the clinical trial process

  6. Defining Quality • Acceptable control of variation • Sources of variation • Trial conduct problems • Poor record keeping • Flawed procedures • Quality is ? - metrics of acceptable variation

  7. Sources of measurement error variability that can contribute to variability in estimates of treatment effect / response Auditing strategy vs. quality assurance strategy Investigator

  8. What does this have to do with the training of clinical investigators ? • No requirement to be trained in clinical trials • No requirement to be trained nor certified in ‘Good Clinical Practices’ • Is a requirement to have a license to practice medicine • Investigators often are the measurement instrument of treatment response and they implement the protocol • Impact they have on the conduct of the study • Whose responsibility - sponsor, monitor, investigator ?

  9. The regulatory review process often serves as the end product audit • FDA evaluates the study report and the conduct and key metrics of quality • FDA evaluates statistical displays of key sources of variation, bias and uncertainty • Regional and site outcomes evaluated: Dropouts, differences in response rates, outcomes, covariates, exposures, follow-up • Individucal patient profiles nested within sites - which sites and which patient records to audit

  10. Some Experience with statistical reviews of NDA’s and Clinical Studies • Summary of review of 7 years of clinical studies involving foreign clinical data in NDA’s • 21 NDA submissions whose decisions depended upon analysis and interpretation of treatment effects in multi-regional trials • John Lawrence evaluation of large cardiovascular outcome studies

  11. Of 1,926 clinical trials analyzed by OB during FY01-FY07:41% were domestic; 50% foreign-domestic; and 9% foreign.Of all subjects enrolled in these trials:30% were U.S.; 63% domestic-foreign; and 7% foreign.

  12. Of 1,926 trials analyzed by OB Statisticians during FY01-FY07:Trend toward increasing numbers in participation of non-U.S. centers and subjects in trial.

  13. Regulatory consequences • Non approvals • 4 of 22 not approved because of regional heterogeneity • 9 of 22 approvable but more information needed - regional heterogeneity • Need another study • Labeling limitations or information - Merit

  14. Study Undertaken by FDA statisticians to evaluate possibility of systematic regional differences • Major cardiovascular outcome studies evaluated over the last 10 years • Overall study result statistically positive, ie. demonstrated overall effect • Region never pre-specified as a factor to be evaluated statistically • 16 independent studies

  15. Estimates and confidence intervals for difference between US and Non-US treatment effects for each study In 13 of 16 , US log hazard above 0 J. Lawrence

  16. An Example: Toprol -XL • Taken from the Current Drug Label ; “Clinical Trials” • MERIT-HF was a double-blind, placebo-controlled study of Toprol-XL conducted in 14 countries including the US. It randomized 3991 patients (1990 to Toprol-XL) with ejection fraction </= 0.40 and NYHA Class II-IV heart failure attributable to ischemia, hypertension, or cardiomyopathy. The protocol excluded patients with contraindications to beta-blocker use, those expected to undergo heart surgery, and those within 28 days of myocardial infarction or unstable angina. The primary endpoints of the trial were (1) all-cause mortality plus all-cause hospitalization (time to first event), and (2) all-cause mortality.

  17. The trial was terminated early for a statistically significant reduction in all-cause mortality (34%, nominal p=0.00009). The risk of all-cause mortality plus all-cause hospitalization was reduced by 19% (p=0.00012). The trial also showed improvements in heart failure-related mortality and heart failure-related hospitalizations, and NYHA functional class. The table below shows the principal results for the overall study population. The figure below illustrates principal results for a wide variety of subgroup comparisons, including US vs. non-US populations (the latter of which was not pre-specified). The combined endpoints of all-cause mortality plus all-cause hospitalization and of mortality plus heart failure hospitalization showed consistent effects in the overall study population and the subgroups, including women and the US population. However, in the US subgroup and women, overall mortality and cardiovascular mortality appeared less affected. Analyses of female and US patients were carried out because they each represented about 25% of the overall population. Nonetheless, subgroup analyses can be difficult to interpret and it is not known whether these represent true differences or chance effects.

  18. A figure From the label

  19. Interpretation - Extrapolation • Impact on composite endpoint • Impact on components of composite endpoints by region / subgroup • Which factor (s) most important to evaluate relationship of treatment effects • Site/center/clinic, Country , Region Wedel, DeMets, Deedwania, Fagerberg, et al. Challenges of subgroup analyses in multinational clinical trials: Experiences from the MERIT-HF trial. Amer. Heart J 2001; 142: 502-11

  20. Antiepileptic Drugs and Suicidality: Statistical Review Mark Levenson, Ph.D. Statistical Safety Reviewer Quantitative Safety and Pharmacoepidemiology Group Division of Biometrics 6/CDER/FDA Joint Meeting of Peripheral and Central Nervous System Drugs Advisory Committee and Psychopharmacologic Drugs Advisory Committee July 10, 2008

  21. Suicidal Behavior or Ideation Odds Ratio Estimates by Location Risk lower in N A

  22. Rates are higher in North America sites

  23. Rates are higher in North America sites

  24. Guidance on the topics • ICH E3 -Multicenter studies - reporting • ICH E9 - Multicenter studies - Planning and Analysis • ICH E5 - Multiregional clinical trials - global drug development - bridging • Literature -

  25. Modernizing the statistical planning of a multi-regional study with more realistic objectives • Some ideas and work of Dr. Hung • Modern planning should rely more on simulations of a variety of assumptions for known or expected sources of variability and heterogeneity - scenario planning

  26. Bridging Region I Region k Global Multi-regional trial

  27. Global Trial Consideration K geographical regions nh: sample size of region h N =  nh yh | h  N( h , 2/nh ) h  ( , 2 ) Effect sizes vary but are all positive Hung, 2007

  28. Question: Is  meaningful, i.e., interpretable for all regions? (Interaction ?) If not, only h is applicable to region h. Then, the study will require a sufficient sample size for each region. Hung, 2007

  29. If  is interpretable for each region, estimate  by Y   rhyh , ( rh=nh/N ) Hung, 2007

  30. Should plan N to detect  =  > 0 at level  & power 1-, assuming   0 Hung, 2007

  31. If, instead,  = 0 is assumed for planning sample size, then the resulting sample size N0 may be too low. How low? Hung, 2007

  32. Studies will be underpowered for effect sizes and could fail because of it - increase size =0.025, =0.1, K=5, (r1 r2 r3 r4 r5)=(.2 .1 .4 .1 .2) Hung, 2007

  33. Clinical endpoints that may be impacted by regional differences • Difficulty with diagnosis , with ascertaining progression or resolution of condition • Anti-bacterial drugs for hospital- acquired pneumonia (HAP) and ventilator-associated pneumonia (VAP) • Creation of composite endpoints whose components are evaluated differently • Antibiotic resistance, clinical practice • Patient reported outcomes, symptomatic conditions • Safety outcomes (how ascertained or defined - suicides)

  34. Non-inferiority trials vs. Superiority trialsImpact of heterogeneity • Timing of outcome measurement • Investigator training • Differential sensitivity and specificity

  35. Reasons for concern when extrapolating • Regional differences in observed treatment effects within the same study (not always clear what is responsible, chance ?) • Differences in results of separate independent studies , each done in different regions • What (bridging data) can explain the differences ? • information gained prior to the studies • information gained after studies completed • A new study

  36. The Way ForwardSome Recommendations to consider • For every multi-regional study, create a common template for planning for homogeneity/heterogeneity of regional differences and exploring sample sizing according to assumptions of dropouts, follow-up, compliance, event ascertainment by investigator, degree of internal consistency • Enhance all study reports with section that discusses process of quality assurance, data management, quality of data collected, monitoring strategies, important descriptors and outcomes by region/country • Improve the statistical analysis plan to specifically address strategies and interpretation of heterogeneity, power, internal consistency of by region results • Address the training / certification of investigators and quality checks • Auditing strategies, metrics of quality • Update the study report for a MRCT to include new issues

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