(2010) Outsourcing of Clinical Trials to Developing Countries
Real World Trials: Uncovering the demand for outsourcing ... · PDF fileReal World Trials:...
Transcript of Real World Trials: Uncovering the demand for outsourcing ... · PDF fileReal World Trials:...
Real World Trials: Uncovering the
demand for outsourcing post clinical
testing
Wednesday 1st July 2015
OCT UK London
Dr Amr Radwan European Medical Director
Norgine Ltd.
Overview
Real World Evidence
What it is and
How it differs from the RCT generated data
Key differences and similarities vis the RCT processes
Why do we need real world evidence ?
Success factors for real world trial process
From concept to reporting and evidence utilisation
Examples from the Real World
RCT data vs Real World Evidence
RCT
Gold Standard of research methodologies
Highly resource intensive
Randomisation a key feature
Tightly defined patient population (multiple inclusion / exclusion criteria)
hence potentially limited applicability of results
Focus on establishing safety & efficacy (esp for licence supporting
submissions)
Use of placebo control / active comparator often needed
RCT data vs Real World Evidence
RWE
More representative of real effects seen in standard clinical practice
(inclusion / criteria minimal compared to RCT)
Relatively low cost and complexity
No randomisation (generally)
Focus on epidemiology / disease and patient characterisation /
effectiveness / cost-effectiveness
No use of placebo comparators can be the patient themselves (pre-
post designs) or cohort matched controls
Use of statistical methods
Why Real World Evidence
Increasing realisation that RCT data is necessary but often not practically
achievable and certainly not sufficient for important clinical and particularly
pricing & reimbursement decision making
Need to make use of large clinical and health system data bases to gather
insights on specific diseases, patient pathways and natural history, identify
unmet needs and characterise patient populations
Availability of increasingly powerful computational tools and analytics
Increasing focus of reimbursement bodies and payors on systematic initial
evaluation of effectiveness and cost-effectiveness of new interventions in
their healthcare systems and monitoring this post reimbursement.
Need for systematic approach to safety signal detection and evaluation to
optimise Benefit-Risk ration for medicinal products and devices.
Real World Evidence why we need it..
Pricing
reimbursement
& Market
Access
HEOR &
Medical Affairs
Patient Safety
Signal
Detection and
Evaluation
Patient
Journey
Resource
Use
DUS
Epi
Studies
Pre /
Post
Studies
Cohort
control
studies
EMR /
database
studies
Registry
Patient Popn
Characterisation
Real World Evidence Generation Examples from the real world 1
Data base interrogation to understand the impact of a condition on disease
progression / natural history / epidemiology. [eg. Use of CPRD &
HES anonymised patient clinical database to evaluate effect of
decompensations on mortality and hospital bed days.
Real World Evidence Generation Examples from the real world 2
Drug Utilisation study, prescription database interrogation
characterisation of patients receiving therapy in the real world.
[eg. Use of pharmacy prescription database to evaluate use in unlicenced
population (eg. children, off-label use, etc.. )
Retrospective real world non-interventional study to evaluate the real
world impact on resource use. (pre/post design)
Prospective real world non-interventional study to evaluate the real world
impact on key patient outcomes and long term resource use. (combined
pre/post and matched cohort design)
APEX Study - Retrospective data collection Pre/Post design
Data captured retrospectively from patient records and other linked data bases (e.g. HES) :
Evaluation of frequency and duration of hospitalisation
12 months 12 months
Patient not on Xolair (PRE) Patient on Xolair (POST) Data
collection
start
PROS
Relatively quick
Good patient characterisation
Better suited to well recorded
data
Patient acts as their own
control
CONS
High risk of missing data in
historical patient notes
Risk of selection bias need to
be carefully managed
Better suited to well recorded
data
PROSPER Study prospective & parallel data collection
Data will be captured from patients on:
Rifaximin- (treatment arm)
Not receiving rifaximin- (comparator arm)
Collect retrospective data on ALL patients once enrolled in the study
The benefit of this design is that two data analyses can be planned:
Prospective comparisons across treatment groups, with matched control for stage of disease progression (the cohort control analysis)
Historical longitudinal comparisons within individual patients (the pre/post analysis)
12 months 24 months
Prospective Data - Control
Study Entry
Retrospective Data
Prospective Data - Treatment Retrospective Data
Analysis 1 Analysis
2a
Analysis
2b
Analysis 3
Market Access in todays pharmaceutical market is driven by the ability of a product to demonstrate value
Value = Outcome / Cost
Payers are increasingly implementing value-based programs: Value-based pricing for pharmaceutical companies
Value-based purchasing for hospitals and providers
As such, value needs to be defined, measured, predicted and optimised by all stakeholders, especially manufacturers
Developing a positive value proposition for products early may help ensuring financially successful development in line extensions
A centerpiece of Market Access is around the concept of Value where RWE can add important information.
Spend time defining your evidence needs first What will you use the data for. What is the minimum you require and what adds value
Evaluate fitness for purpose of potential provider
Local/national focus vs international capability
What will fulfil the need vs bells & whistles
Speed of delivery / Quality of output / Cost triangle
Prioritisation
Clearly brief the providers on the expected data needs and timelines
Does the provider understand the differences in approach in RWE vs RCT ? / level of experience ?
Successful outsourcing in RWE generation key principles
Have a robust process in place to access capability and engagement Outsource, but take responsibility for process & outcomes
Ensure appropriate level of governance in place.
Statistical methods to minimise bias are appropriately well defined in SAP (eg propensity scoring, matching ratios, estimated sample size calculations etc.)
Formal ethics review often not a requirement but should be sought where applicable.
Manage joint ownership of sites throughout the study periods and maintain close comms with all relevant stakeholders
Have a communication / publications plan draft one early in the process and refine as time and needs progress
Successful outsourcing in RWE generation key principles
CASE Study
A personal perspective from recent experience TARGAXAN (rifaximin-a) for Hepatic Encephalopathy
Very little known about this severe complication of liver disease (often an indication for transplantation!)
Standard of Care Lactulose (>40years old)
Relatively little general interest in disease area (compared to other novel agents e.g. New Oral Hep C therapies )
1 High Quality pivotal study in US/Russia/Canada Orphan indication in the USA EU licence based on this but no EU patients in the data set.
First true specialist product launch for the company No previous NICE experience
Success factors
True collaboration of internal functions (Medical, MAx and Commercial) to identify key data gaps for the different work-streams
Scientific / KOL / patient org. engagement converting what we dont know we dont know, to what we know we dont know (defining the gaps)
Significant generation (and publication ) of Key Epidemiology data (mortality of the disease / impact on resource use) Real World Evidence of resource use reductions associated with Targaxan
use in recurrent HE retrospective data 1 year pre & post analyses
Informed the building of a fit for purpose Health Economic Model to more closely reflect the natural history of the condition
One stage Markov model 2 stage Markov model
CPRD analyses (combined with pivotal study data review)
helped informed the building of a fit for purpose Health
Economic Model
One stage Markov model 2 stage Markov model
Stage 1
Stage 2
Stage 1 Stage 2
Impact of RWE on modelling approach for HTA
The Journey
Just over 2 years from early scoping to FAD
4 x TAC Technology Appraisal Committee meetings (D)
Significant peri-licencing experience
generating compelling real world evidence of value (hospitalisation frequency