Transcript RANDOMISED Clinical Trials

El maravilloso mundo de la estadística en
la industria farmacéutica: instrucciones,
interacciones y contraindicaciones
Xavier Núñez,CStat
Senior Statistician
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
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
 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 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
Distribution of client segments in 2010
*”Other” includes: Academia, Diagnostics, Nutrition, Laboratory/GLP
Based on 129 unique client companies during 2010
20 largest customers in 2010
Project delivery functions
Global Biometrics
Director
Global Project Delivery
Director Global BIM
Unit Manager BIM
South Europe
DM /CDA
Prog .
Unit Manager BIM
West Europe
Unit Manager BIM
Northern Europe
Stat .
DM /CDA
Prog .
Stat .
DM /CDA
Prog .
Stat .
,
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
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)
TFS Spain Biometrics
Mette Ravn
Director Global BIM
Rosa Alonso
Unit Manager BIM Spain
DM/PROG/CDA
STATS/SAS PROG.
Emma Albacar
Xavier Núñez
Anna García
Mario Pircher
Juani Zamora
Marta Figueras
Marta Gutierrez
Daniel Mosteiro
Eva Usón
Elisabeth Roqué
Cristina López
Ramon Dosantos
Verónica Ortega
Mireia Cuellar
Data Management working flow
DM Plan
DB set up
Test of DB set up
Plausibility checks
CRF
Design
Data review
Query handling
Data update
Reconciliation
Coding of AEs, CMs, MHs
Data Entry Manual
Design Specification
Start of Data Entry
Soft Lock/
DB closure
Unblinding
DB QC
Hard Lock
DM Report
Archiving
Statistics working flow
Ad-hoc
study
related
questions
Study
design
Study
protocol
DB closure
CRF
design
Sample size
calculation
Client review
SAP
DPP
Prepare
statistical
programs
Quality control
Decision
about
analysis
sets, etc
Statistical
report
(Release of
TFLs)
Client review
Clinical
study
report
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
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)
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.
Clinical trials vs. non-interventional studies
No intervention in the study design
- Treatment exposition without participation
of the investigator → ‘observes’ subjects
- No randomisation procedures
OBSERVATIONALS
Intervention in the study design
- Treatment assigned to the subjects
by the investigator
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
- 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.
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
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...)
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
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
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
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
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
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
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’
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
Any Questions?
Thank you for your patience!
WWW.TFSCRO.COM
email: [email protected]
Back-up slides
SAS Programming working flow
QC (Quality Check)
DEV (Development)
SAP
Prepare
statistical
programs
Ready?
Yes
Ready?
Peer review from a
second statistician
Yes
REL (Release)
Validated output
released to client
No
No
Statistician review
Minor or major findings found in the
validation and reported in the QC Plan
No findings in the validation;
QC Plan signed and approved
Back-up slides
Parallel groups
Study
group
Control
group
First visit
Last visit
Back-up slides
Cross-over groups
Wash-out
period
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
Back-up slides
Factorial design – multiple groups
A+B
A + placebo
B + placebo
First visit
Last visit
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 followup
- Study power is sometimes underestimated