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Statisticians Statistically Significant
Xavier Núñez, CStat
Senior Statistician, CStat
Introduction to:
• CRO and Clinical Trial: definitions
• TFS Company & Organisation
• Data Management & Statistics working flow
• Regulatory guidelines
• Type of clinical trials
• 3 Illustrations: statistically significant?
• Conclusions
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)
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
TFS -Introduction
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Founded in 1996 with headquarters in Sweden
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Worldwide ranking no 14*
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~ 600 employees
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Operations inspected by US FDA, EMA, MHRA
(UK) and MPA (Sweden)
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Geographical coverage in Europe, USA and Japan
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Conducting clinical trials in 40 countries
worldwide (December 2012)
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Projected revenue €75 million in 2013
TFS European locations
TFS global HQ
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Sweden
TFS regional HQ
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Sweden
Spain
The Netherlands
Hungary
TFS country offices
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Norway
Denmark
Finland
Russia
UK
France
Germany
Portugal
Italy
The Baltics (Estonia, Latvia, Lithuania)
Poland
Czech Republic
TFS Solutions for entire clinical development cycle
Phase 0/I and PoC trials;
Phase II – IV, NIS trials; 26 countries world-wide
Clinical research professionals within FSP models;
Specialist training for clinical research professionals;
www.tfsacademy.com
TFS Project delivery functions
TFS Barcelona – Biometrics
Ricard Quingles
Regional Managing Director, South Europe
Emma Albacar
Associate Unit Manager Biometrics,Spain
Eva Lundqvist
Director Global Project Delivery
Rosa Mª Alonso
Unit Manager Biometrics, Spain
Biostatistics
Data Management
Data Assistant
Ramón Dosantos
Senior Statistician
Senior Clinical Data Manager
Cristina López
Senior Clinical Data Manager
Senior Statistician
Mireia Cuellar
Clinical Data Associate
Juani Zamora
Senior Statistician
Senior Clinical Data Manager
Daniel Mosteiro
Senior Statistician
Senior Clinical Data Manager
Elisabet Roqué
Clinical Data Associate
Eva Usón
Senior Statistician
Senior Clinical Data Manager
Judith Oribe
Senior Statistician
Senior Clinical Data Manager
Marta Gutiérrez
Clinical Data Associate
Marta Figueras
Senior Statistician
Rosario Peláez
Statistician
Clinical Data Manager
Verónica Ortega
Clinical Data Associate
Mercè Viladrich
Senior Statistician
Emilio Sánchez
Statistician
Clinical Data Manager
Laura García
Clinical Data Associate
Jordan Bertsch
Senior Statistician
Laia Pujantell
Senior Clinical Data Manager
Maite Ruiz
Clinical Data Associate
Xavier Nuñez
Senior Statistician
Mario Pircher
Senior Clinical Data Manager
Data entry people (variable)
14 Statisticians!!!!
Data Management working flow
Statistics working flow
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 –E6: GCP (1996)
 - EU Directives 2001/20/EC, 2005/28/EC
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
 - ICH –E10: Choice of Control Group in Clinical Trials
 - CDISC Clinical Data Interchange Standards Consortium,
Operational Data model (ODM)
Clinical trials vs. non-interventional studies
No intervention in the study design
Intervention in the study design
- Treatment exposition without participation of the
investigator → ‘observes’ subjects
- No randomisation procedures
- Treatment assigned to the subjects by the
investigator
OBSERVATIONALS
CLINICAL TRIALS
Disease exposition = treatment?
Epidemiological
Disease
No
Yes
Post-Authorisation
study (EPA)
Study medication
RANDOMISED
Clinical Trials
Quasi-experimental
Clinical Trials
(experimental)
(Non-randomised)
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
- Multicentre, Larger sample size, (1000-3000 subjects)
- Objectives: confirm efficacy –superiority, non-inferiority?, no safety issues
 Phase IV (post-authorisation)
- Patients
- Multicentre, non-interventional studies
- Objectives: optimal use of treatment, risk-benefit, marketing, etc.
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
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
Statistically significant?
Importance of estimation and its superiority to significance testing
- If we do not get a significant difference, what can we then conclude? Only that
we have not found evidence to support the existence of a treatment effect
- Estimates may often be better than p-values
Conclusions
Statistically significant
 Become a statistician: open-minded and objective in the assumptions;
precise and analytical in the results. Study Design is crucial !!
 Become a scientific: interact with your clinical colleagues, do not be only
a programmer!
 Communicate – “Statisticians seem to talk double Dutch”: make yourself
and the results understandable to any person with no knowledge of
statistics at all
 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
 Work closely with your team – you need the study input from the
project leader, the clinical expertise from the medical writer, the
knowledge of data from the CRA, and the DB specifications from the DM
Conclusions
Not Statistically significant
 Don’t look for p-values, think statistically!
 “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 ‘suggest that’
We are very lucky!! We are (or will be) statisticians!!!!
Some remarks to end...
-Many people use statistics as a drunken man uses a lamp post; beware of
p-values
-Statisticians are more rigorous in interpreting statistics but physicians are more
imaginative
-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
-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
Any Questions?
Thank you for your patience!
WWW.TFSCRO.COM
email: [email protected]