Introduction to:

1. El maravilloso mundo de la estadística en la industria farmacéutica: instrucciones, interacciones y contraindicaciones Xavier Núñez,CStat Senior Statistician

2. Introduction to: • CRO and Clinical Trial: definitions • TFS Company & Organisation • Global Biometrics • Data Management working flow • Statistics working flow • Regulatory guidelines • Type of clinical trials • Statistical Analyses vs. Clinical trials • Examples of clinical trials • Day-to-day example • Conclusions

3. What is a CRO? • Chief Risk Officer • Cathode Ray Oscilloscope • Cro-Magnons • Clinical Research Organization: a service organization that provides support to the pharmaceutical and biotechnology industries in the form of outsourced pharmaceutical research services (for both drugs and medical devices)

4. What is a Clinical trial? A clinical trial is a research study to answer specific questions about vaccines or new therapies or new ways of using known treatments. Clinical trials (also called medical research and research studies) are used to determine whether new drugs or treatments are both safe and effective

5. Founded in 1996 with headquarters in Sweden Largest non-listed European clinical CRO – worldwide ranking no 14* ~ 500 employees Operations inspected by US FDA, EMEA and Swedish MPA Geographical coverage in Europe, USA and Japan Operations in 4 business areas Conducting clinical trials in 28 countries worldwide (Dec 2009) Projected net revenue 54 million USD in 2010 *Based on the Investment Bank William Blair & Company report – net revenue estimations 2008 for clinical CROs TFS -Introduction

6. TFS European locations • TFS global HQ • Sweden • TFS regional HQ • Sweden • Spain • The Netherlands • Hungary • TFS country offices • Norway • Denmark • Finland • Russia • UK • France • Germany • Portugal • Italy • The Baltics (Estonia, Latvia, Lithuania) • Poland • Czech Republic

7. Distribution of client segments in 2010 Based on 129 unique client companies during 2010 *”Other” includes: Academia, Diagnostics, Nutrition, Laboratory/GLP

8. 20 largest customers in 2010

9. Project delivery functions

10. Global Biometrics Director Global Project Delivery Director Global BIM Unit Manager BIM Unit Manager BIM Unit Manager BIM South Europe West Europe Northern Europe DM / CDA Prog . Stat . DM / CDA Prog . Stat . DM / CDA Prog . Stat . ,

11. Global Biometrics - Services • TFS Global Biometrics offer: • Biostatistics, Programming and Clinical Data Management • Currently 40 employees working in Global Biometrics (Spain, Sweden, Netherlands and Denmark) • Support for Life Science projects • Clinical trials, phases 1-4 • Evaluation of Medical device, diagnostic test • Non-interventional studies • Software: SAS, SPSS, Minitab, Access, NQuery, Ene... • By establishing a sound approach to clinical biostatistics and clinical data management during the planning stages of the clinical development program we: • Improve the quality of submissions • Accelerate timelines • Decrease costs • Reduce risks

12. Global Biometrics - Services • Clinical Data Management • Case report form (CRF) / eCRF design • Database and Data Entry solutions • Statistical services & consultations • Input to study design • Randomisations • Statistical analysis plan (SAP) • SAS programming: tables, figures and listings (TFLs), statistical analyses, standard macros... • Statistical analysis and report • Support with publications & clinical study report (CSR) • Support with CDISC standards SDTM and ADaM formats • Training via TFS Academy • CPS (contract placement services)

13. Mette Ravn Director Global BIM Rosa Alonso Unit Manager BIM Spain DM/PROG/CDA Anna García Mario Pircher Daniel Mosteiro Marta Gutierrez Elisabeth Roqué Cristina López Verónica Ortega Mireia Cuellar TFS Spain Biometrics STATS/SAS PROG. Emma Albacar Xavier Núñez Juani Zamora Marta Figueras Eva Usón Ramon Dosantos

14. Data Management working flow Data review Query handling Data update Reconciliation Coding of AEs, CMs, MHs DM Plan DB set up Test of DB set up Plausibility checks Soft Lock/ DB closure Unblinding Hard Lock DM Report Archiving Data Entry Manual Design Specification Start of Data Entry DB QC CRF Design

15. Statistics working flow Ad-hoc study related questions DB closure Clinical study report Study protocol Sample size calculation CRF design Statistical report (Release of TFLs) Decision about analysis sets, etc Study design SAP DPP Prepare statistical programs Client review Client review Quality control

16. Medical research - Regulations • Good Clinical Practice (GCP) • An international ethical and scientific quality standard for designing, conducting, recording and reporting trials that involve the participation of human subjects • The most important sources for GCP-compliant guidelines referring to the EU are the following: • - Declaration of Helsinki (1964) • - ICH GCP –E6 (1996) • - EU Directive 2001/20/EC • - EU Directive 2005/28/EC

17. Medical research - Regulations • Additional guidelines refer to specific statistical or DM regulations or to other recommendations, such as • - ICH –E9: Statistical principles for clinical trials • - ICH –E3: Structure and contents of clinical study reports • - Good Clinical Data Management Practices • - CDISC Clinical Data Interchange Standards Consortium, Operational Data model (ODM)

18. Specific FDA Issues • The FDA is the US Government regulatory office for registration of Pharmaceutical products. Here especially the Code of Federal Regulations (CFR) applies, which is the codification of the general and permanent rules published in the Federal Register by the agencies of the Federal Government. FDA regulation is relevant for EU projects in development of drugs considered for possible registration in the US. • However, it must be clarified, that in the EU it is not the FDA regulations which are governing, but the national implementations of EU directives or the EMEA/EMA implementations of EU Regulations.

19. OBSERVATIONALS Epidemiological Disease Post-Authorisation study (EPA) Study medication RANDOMISED Clinical Trials (experimental) Clinical trials vs. non-interventional studies No intervention in the study design - Treatment exposition without participation of the investigator → ‘observes’ subjects - No randomisation procedures Intervention in the study design - Treatment assigned to the subjects by the investigator CLINICAL TRIALS Disease exposition = treatment? No Yes Quasi-experimental Clinical Trials (Non-randomised)

20. Type of clinical trials • Phase I • - Healthy volunteers • - Small sample size (6-30 subjects) • - Usually FTIH • - Objectives: safety (adverse events), dose range, PK/PD • Phase II • - Healthy volunteers / Patients • - Larger sample size (20-300 subjects) • - Objectives: efficacy, safety, dose-response • Phase III • - Patients • - Huge sample size, multicentre (1000-3000 subjects) • - Objectives: confirm efficacy –superiority?, no safety issues • Phase IV (post-authorisation) • - Patients • - Objectives: optimal use of treatment, risk-benefit, marketing, etc.

21. Type of clinical trials • By the awareness of treatment administered - Open-label: both investigators and subjects know which treatment is being administered - Single-blinded: investigator is aware of the treatment administered, but the subject is not - Double-blinded: neither investigators nor subjects know which treatment is being administered • By time of observation - Retrospective: data from past records is collected in a unique visit, with no follow-up - Cross-sectional: all present data from subjects is collected at a defined time-point - Prospective: subjects are followed over a period of time, collecting data in different visits • By sequence of treatments - Parallel : subjects are randomly assigned to a unique treatment throughout the study - Cross-over: subjects are randomly assigned to a sequence of treatments

22. Type of clinical trials • By nature of comparator treatment • - Placebo-controlled: a group of subjects receives a ‘placebo’ treatment, which is specifically designed to have no real effect → sometimes is not ethical! • - Active-control: the experimental treatment is compared to an existing treatment → that is clearly better than doing nothing for the subject • By type of comparison • - Superiority: the clinical objective of efficacy is to show that the response to the experimental treatment is superior to the comparator treatment → usually superiority to placebo • - Equivalence or non-inferiority: the clinical objective of efficacy is to show that the response to the experimental treatment is at least as good, or not clinically inferior, to the comparator treatment → usually non-inferiority to active control

23. Statistical analyses vs. clinical trials • Phase I • - Graphical tools (individual PK graphs –Cmax, AUC,...) • - Descriptive analysis • Phase II • - Descriptive and statistical procedures for efficacy • - Oncology: survival analysis (Kaplan-Meier, Cox regression) • - Dose-response models • Phase III • - Modelling techniques for efficacy: adjustment for covariates, multicentre studies, treatment of missing data, multiple comparisons... • Phase IV (post-authorisation) • - Explicative models, correlations and interactions, graphical display (bar chart, pie chart, map areas...)

24. Examples of clinical trials • - A prospective, open-label, non-randomized, clinical trial to determine if xxxx improves ambulatory measures in relapsing-remitting multiple sclerosis (RRMS) patients → phase IV • Pharmacokinetic study of single doses of xxxx, 75 mg and 300 mg, in healthy subjects → open-label, two-treatment crossover, phase I • A multicenter, randomized, parallel, double-blind, dose ranging, placebo-controlled study to compare antiviral effect, safety, tolerability and pharmacokinetics of xxxx monotherapy vs. placebo over 10 days in HIV-1 Infected Adults → phase IIA • Efficacy, safety and tolerability of split-dose of xxxx compared to yyyy solution for colonoscopy preparation: a randomized, controlled trial→ phase III • xxxx plus radiotherapy and Induction Chemotherapy in patients with head and neck cancer → phase II - phase III

25. Day-to-day example • 1. A client contacts me in order to ask me about the sample size calculation and statistical input of a new clinical trial Dear Xavier, I hope you are well. Please find attached a draft version of the SEA Protocol, this is an open-label, randomised, multicentre phase III study in patients with colorectal cancer. The primary endpoint of the trial is the progression free survival. Could you please give us advice on the sample size and the statistical sections of the protocol (the mentioned paragraphs are highlighted in yellow). Looking forward to hearing from you soon, Best wishes, Llorenç Badiella

26. Day-to-day example • 2. The statistician reads the protocol, look for references about the disease and clinical variables/endpoints used for those specific area, checks the study assumptions and primary endpoint, and from these information, estimates the sample size and writes the statistical section of the protocol Dear Llorenç, Thank you for your email. Please find attached the SEA Protocol with my input. The sample size calculation resulted in the following: to achieve a 80% power to detect differences in the contrast of the null hypothesis Ho (Equality of the progression-free survival curves between groups) through a Log-Rank test for two independent samples bilaterally, with a significance level of 5% and assuming that the probability of PFS at 24 months will be 30% for the reference group, and 45% for the experimental group, a total of 454 subjects (227 in each group) will be required. Best regards, Xavier

27. Day-to-day example • 3. Sometimes, the client gets back to the statistician as the sample size estimated is too high for • The company resources, or • The recruitment expectation • In this situations, new strategies are required, which normally imply to • Increase the expected clinical difference, or • Change the primary endpoint

28. Conclusions • Instructions: • Become a statistician: open-minded and objective in the assumptions; precise and analytical in the results • “They want to believe”: be responsible, our work is key in the outcome of a clinical trial ; the client will listen to you and act from the results you present • Teach and be taught, and share your knowledge with your colleagues • Recycle yourself: statistics are a dynamic matter, self-study, training courses and new guidelines are a must do • Follow GCPs, regulatory requirements and company’s SOPs

29. Conclusions • Interactions: • Work closely with your team: you need the study input from the project leader, the clinical expertise from the medical writer, the knowledge of the data from the CRA and CDA, and the DB experience from the DM • “One step forward, three steps back”: do not move on without the OK from the client: sometimes it can turn against you • “Statisticians seem to talk double Dutch”: make yourself and the results understandable to any person with no knowledge of statistics at all

30. Conclusions • Contraindications: • Learn to say NO: sometimes it is not possible to do everything the client ask us to do • “You don’t know the power of the dark side”: if your study is underpowered or you carry out statistical analysis of secondary endpoints, beware of the conclusions: the results do not ‘conclude that’ but the ‘suggest that’

31. Some remarks to end... • Biostatisticians are always talking about power but do not have any • -Statisticians expect the average but on average people do not expect statisticians • -An idiot with a computer is often more powerful than a statistician with a pencil • -Statisticians worry about interactions and this often makes them lonely • -Even if you have a significant relationship with a statistician you may not find it relevant • Guernsey McPearson • http://www.senns.demon.co.uk/Confuseus.htm

32. Any Questions? • Thank you for your patience! WWW.TFSCRO.COMemail: xavier.nunez@tfscro.com

33. REL (Release) DEV (Development) Validated output released to client Yes Yes Prepare statistical programs Ready? Ready? No Statistician review No findings in the validation; QC Plan signed and approved Minor or major findings found in the validation and reported in the QC Plan Back-up slides SAS Programming working flow QC (Quality Check) SAP Peer review from a second statistician No

34. Back-up slides Parallel groups Study group Control group Last visit First visit

35. Wash-out period Back-up slides Cross-over groups

36. Back-up slides • Advantages of Cross-over groups: • Reduction of variability → each subject is his own control –no within-subject variability • Study design is more efficient, allows for a smaller sample size • Inconvenients: • - Wash-out period may not exist or may be difficult to calculate

37. Back-up slides Factorial design – multiple groups A + B A + placebo B + placebo Last visit First visit

38. Back-up slides • Advantages of factorial designs: • Efficiency of study design → allows to respond two or more questions in the same trial • Inconvenients: • Complex design, difficulty of treatment-compliance and follow-up • Study power is sometimes underestimated