Improving FDA/Industry Interactions: Suggestions from FDA/CDER Statisticians

1. Improving FDA/Industry Interactions: Suggestions from FDA/CDER Statisticians Rafia Bhore, Ph.D. Janice Derr, Ph.D. FDA / CDER / Office of Biostatistics The views expressed in this presentation are those of the speakers and not necessarily of the U.S. Food and Drug Administration (FDA).

2. CDER Office of Biostatistics

3. Micro Chem Stats Clinical Pharm/Tox ClinPharm Communication Dynamics between FDA and Industry Industry Project Team Project Manager Regulatory Affairs

4. Interactions During Drug Development Clinical Start NDA/BLA Submission NDA Review Pre-clinical Research Phase I Phase II Phase III Pre-IND Meeting EOP II Meeting Pre-NDA Meeting Labeling Meeting

5. Good Meeting Management Practices GMMPs Facilitate input from review disciplines • Prior to internal meeting (draft responses to industry questions) • Internal meeting (preliminary comments to sponsor) • Formal meeting (moderated discussion) • Follow-up (meeting minutes, further discussion)

6. Suggestions from CDER Statisticians(Good Meeting Practices) • Use a meeting with FDA as an opportunity to send in questions about statistical issues • Ask good questions that will give you useful answers • Provide sufficient detail to help us give useful statistical review comments • Use the channels of communication to get a response from FDA statisticians about statistical issues

7. Investigational New Drug Application (IND) Stage Special Protocol Assessment Statistical Analysis Plans

8. Special Protocol Assessment SPA Guidance 2002 • Sponsors can submit certain types of protocols with specific questions prior to start of study (Guidance recommends 90 days). • FDA determines if SPA process applies to the request, and if so, responds to questions within 45 days (PDUFA goal). • Protocol agreements under SPA are part of the administrative record. Regulations describe the circumstances under which the agreements can be changed.

9. Suggestions from CDER Statisticians(Special Protocol Assessment) • Ask good questions that will give you useful answers • Provide sufficient detail to help us give useful statistical review comments

10. Statistical Analysis Plan (SAP) • Prospective plan of statistical methods not detailed in the Protocol • Protocol details design considerations vs. SAP details analysis considerations • Design: Endpoints, type of control, planned comparisons, multiple testing, interim analyses • Analysis: Statistical models, handling of missing data, nature of censoring, analysis populations, repeated measurements over time, study windows, etc.

11. Suggestions from CDER Statisticians(Statistical Analysis Plan) • Statistical Analysis Plan (SAP) should be detailed and prospectively written • Prospectively submit to FDA for Phase 3 studies and Phase 2 supportive studies • Open-label studies submit before study begins • Blinded studies submit prior to last patient enrolled or first interim analysis (whichever comes first)

12. Suggestions from CDER Statisticians(Statistical Analysis Plan contd.) • Identify critical issues at protocol design stage or at least Statistical Analysis Plan writing • Examples: adjustment for multiplicity, interim analysis plan, non-inferiority evaluation, missing data, … • Commercial Sponsors should encourage co-operative trialists to write a Statistical Analysis Plan

13. New Drug Application (NDA) Stage Integrated Summary of Efficacy Labeling

14. Integrated Summary of Efficacy (Suggestions from CDER Statisticians) • Important component of New Drug Application Review • Provide clinically meaningful and logically tight argument whether drug has necessary evidence for efficacy claim • Provide side by side comparison of studies • NOT necessarily pooled or meta-analysis of efficacy • Discuss pooling study results with FDA

15. ? Let's discuss Integrated Summary of Efficacy Example: CanYoUPRove Efficacy and Safetyof curevir(CURES)

16. Statistical Input on Labeling Text FDA Statisticians review labeling text: • Statistical support for study conclusions, claims and indications • Description of study results, summary statistics and inferential language • Information in tables and figures

17. Labeling Example #1: Statistical Input Provided CLINICAL STUDIES … The NAGLAZYME-treated group showed greater mean increases in the distance walked in 12 minutes (12-minute walk test, 12-MWT) and in the rate of stair climbing in a 3-minute stair climb, compared to the placebo group (Table 2).

18. Labeling Example #2: Statistical Input Needed Proposed text: “The combination of A and B is effective in lowering LDL-C levels beyond that achieved by either agent alone.” Statistical issue: The study was not designed to support this conclusion. The study had two arms, (A+B) combination product, and A monotherapy.

19. Labeling Example #3: Statistical Input Needed Proposed table: The symbol “*” was used for p<0.05, and “**” was used to indicate no statistically significant difference between the active treatment arm and the placebo arm. Statistical issue: This is not a typical way to depict this outcome and may be confusing to some readers.

20. Suggestions from CDER Statisticians(Labeling) • Provide your statistical perspective in the development of labeling text. • Labeling Guidance, 2006: • Clinical Studies Section • Adverse Reactions Section

21. Micro Chem Stats Clinical Pharm/Tox ClinPharm Improving Statistical Communication • Provide statistical input at all stages • Ask good questions • Provide detailed, timely information • Address critical statistical issues Industry

22. Acknowledgments • FDA Statisticians from • Divisions of Biometrics 1, Biometrics 2, Biometrics 3, Biometrics 4, and Biometrics 5 • Industry Statisticians /Programmers • for their promptness in responding to FDA questions!