SVMPharma Real World Evidence – The RWE Series – Part II: Collecting Real World Evidence

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RWE TRIALS
BIG DATA
CUSTOM DATA COLLECTION
THE RWE SERIES
Providing the background,
the research and the insights.
PART II:
COLLECTING REAL WORLD
EVIDENCE

RWE Series Part II: Collecting Real World Evidence
© 2015 SVMPharma Ltd. All rights reserved.
2
 To continue
turn to Page 5
 To continue
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 To continue
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The Big Data Route
Taking this path you will access
vast and comprehensive
datasets to collect data at a scale
far beyond what would be
feasible in a conventional clinical
trial. Using powerful data tools,
the filtering and selection of data
within millions of records can be
performed efficiently. Using
search and filtering tools and via
combining datasets you can
zoom into specific data to answer
your questions and discover new
opportunities.
The RWE Trial Route
If you choose this path you will
find the established and
accomplished group of study
designs known as observational
studies. For decades these
studies have shed light on the
real-world impact of treatments
and services. Alongside these
studies you will find their newly
established neighbour, pragmatic
trials. Find out how pragmatic
trials adapt the existing
methodology of RCTs to add
fresh insight to real-world
effectiveness.
The Custom Data Collection Route
Heading in this direction, you will
uncover a unique approach to
collecting RWE. You will find a
custom-built online treatment
evaluation programme, designed
and developed alongside an
expert group. Healthcare
professionals (HCPs) across
selected centres enter data
retrospectively from medical
records and data can be
prospectively entered as it
becomes available. You will
discover the positive outcomes
derived from this method and the
additional benefits which it
brings.
THE ROADS TO RWE

3
1) The Big Data Route
Big Data is a collection of large and complex
digital datasets which typically require non-
standard computational facilities for storage,
management and analysis. In recent years
Big Data has become a buzzword across many industries; this is driven by the increased
collection of data throughout our daily lives, the universal digitisation of information and the
increased technological capacity to handle this data (1, 2).
The UK is renowned for a number of comprehensive healthcare-based datasets, here we will
focus on four key RWE data sources: Hospital Episode Statistics (HES), Clinical Practice
Research Datalink (CPRD), GP Prescribing Data and the Quality Outcomes Framework
(QOF). It is important to note that the majority of these datasets cover England only (with
the exception of CPRD); the devolved nations have their own equivalents (e.g. Scottish
Morbidity Record (SMR) or Patient Episode Database Wales (PEDW)).
HES and CPRD have been used for research for
over 25 years, and have supplied the data for
hundreds of publications. However, it is much more
recently that the technology has been available to
realise the full potential of these datasets; you can now search, sort and interpret this data
with increasing speed and efficiency. There is a continuing effort to increase data linkage;
CPRD can be linked with HES and other national datasets including the Cancer Registry and
Cardiovascular Outcomes (3, 4).
Recently, GP Prescribing Data has been used alongside incidence rates from QOF data to
map spending on metformin and methylphenidate. Maps have been created to a high
THE UK IS RENOWNED FOR A
NUMBER OF COMPREHENSIVE
LARGE DATASETS
10
100
1,000
10,000
100,000
1,000,000
10,000,000
100,000,000
C u s t o m D a t a
C o l l e c t i o n
R W E T r i a l Q u a l i t y
O u t c o m e s
F r a m e w o r k
( Q O F )
G P
P r e s c r i b i n g
D a t a
C l i n i c a l
P r a c t i c e
R e s e a r c h
D a t a l i n k
( C P R D )
H o s p i t a l
E p i s o d e
S t a t i s t i c s
( H E S )
NUMBEROFRECORDS
DATA SOURCE
H O W B IG IS 'B IG D ATA'? A SIZ E C O MPARISO N
A COLLECTION OF LARGE AND
COMPLEX DIGITAL DATASETS
THE UK IS RENOWNED FOR A
NUMBER OF COMPREHENSHIVE
HEALTHCARE-BASED DATASETS

4
spatial resolution and combined with demographic and geographic data. This data can be
analysed to distinguish between chance fluctuations and genuine differences in prescribing
rates, and users can accurately identify where action is required (5).
The table below shows the types of data available in the four key datasets, where to find it,
and how you can access it.
Hospital Episode Statistics
(HES)
GP Prescribing Data Clinical Practice Research
Datalink (CPRD)
Quality Outcomes
Framework (QOF)
Held by Health and Social Care
Information Centre (HSCIC)
Health and Social Care
Information Centre
(HSCIC)
Medicines and Healthcare
Products Regulatory
Agency (MHPRA)
Health and Social
Care Information
Centre (HSCIC)
Contents Hospital inpatient, outpatient
and A&E activity with up to
300 fields per record.
Includes Patient Reported
Outcome Measures
(PROMS). Data submitted
to the Commissioning Data
Set (CDS) for monitoring
and payment purposes and
this data is input and coded
from patient notes and
hospital records.
Practice level
prescription information
for dispensed items
including BNF code,
cost, quantity and
geographic information
of where it was
prescribed.
Longitudinal primary care
data and includes
diagnoses and symptoms
irrespective of
hospitalisation in addition
to drug prescriptions,
vaccinations, blood test
results and risk factors.
The data is recorded using
the Read Coded Clinical
Terms system of nearly
100,000 codes (4).
Practice level
information of
prevalence and
achievement based
on targets updated
yearly. Designed to
encourage GP
behaviours and
focus towards the
prevention and
management of key
chronic diseases.
Areas
Covered
England (devolved nations
have their own equivalents)
England (+Wales since
2011 and Northern
Ireland since 2013)
UK England
Years
Covered
1987-present April 2010-present 1987-present (previously
known as GP Research
Database GPRD)
2004
Size 125 million records added
yearly, where one record is
a period of care/ finished
consultant episode
Approximately 4 million
rows of data every
month, where a row is a
specific drug preparation
in combination with a
specific GP Practice
5 million active patients
(13 million overall)
7921 GP Practices
2013-2014
Access Direct from HSCIC via
license application or via
intermediary
Freely available Application only. In-house
researchers work on
behalf of applicant, no
direct access to data
Freely Available
You will find that Big Data in healthcare is a highly
discussed topic where there is great potential
tempered by a number of concerns regarding
confidentiality. The use of these large datasets in
healthcare faces more obstacles and scrutiny than ever before, which has led to ongoing
changes in the structure and availability of the data (6-8).
In 2013 the care.data programme was developed which would add pseudonymised GP
records through the General Practice Extraction Service (GPES) to HES data. This is
currently undergoing a pathfinder phase after initial delays (9, 10).
Destination RWE:  or turn to page 8
To take another route  or turn to page 2
A GUIDE TO THE KEY SOURCES OF BIG DATA
THERE IS GREAT POTENTIAL
TEMPERED BY A NUMBER OF
CONCERNS

5
2) The RWE Trial Route
Designing and conducting a trial is an important and robust way of collecting RWE with a
significant amount of flexibility. RWE trials can be divided into two main groups,
observational studies and pragmatic trials. Both types use existing or adapted study designs
but shift the emphasis towards external validity in order to gain better understanding of real
world practice.
Observational Studies
In observational studies the
assignment of subjects into a
treatment group and control group
is outside the control of the
invigilator and these studies draw
inferences about the possible
effects of treatment on a subject.
Observational research has a long
and successful history in
generating RWE, with advocates
of observational studies promoting
its ability to assess real world
practice and outcomes (11).
Both retrospective (case-control)
and prospective (cohort)
observational studies have a role
in obtaining RWE due their external validity (applicability to real world practice). However
observational studies can be slow to setup and inefficient in delivering results (12, 13).
Pragmatic Trials
Pragmatic RCTs (pRCTs) have become
increasingly common in recent years with a
tenfold increase in publications over the last
decade. Pragmatic RCTs are randomised and
follow a similar methodology to a traditional
RCT. However they differ on a number of
counts and use broad eligibility criteria to
ensure the inclusion of subjects who are
representative of real world clinical settings.
The medical management within these trials are consistent with actual clinical care protocols
and they measure outcomes that are important to patients and decision makers (including
functional status, quality of life and costs). Pragmatic RCTs can be more expensive to run
than traditional RCTs, due to the broader scope and increased range of outcomes measured
(14-17).
Observational
Study
Analytical Study (Has
Comparison Group)
Cohort Study
(Exposure known,
outcome unknown)
Case-Control Study
(Outcome known,
exposure unknown)
Cross-Sectional Study
(Snapshot of outcome
and exposure at a point
in time)
Descriptive Study
(No Comparison
Group)
THE TYPES OF
OBSERVATIONAL
STUDY
Pragmatic
Trials
(pRCT)
Explanatory
Trials
(Traditional
RCTs)
ExternalValidity
InternalValidity
INTERNAL AND EXTERNAL VALIDITY

6
3) The Custom Data Collection Route
The approach outlined here uses
retrospective or prospective data from 50-
200 patient health records entered by
health professionals into a web-based tool
or Treatment Evaluator™
. Data entry is
guided and validated; complex patient
histories are streamlined and reduced to
the relevant information.
The Service Evaluation Approach
Clinical data collection programmes can
be classed as research, audit or service
evaluation. Research programmes derive
generalisable new knowledge and
generate and test hypotheses. An audit is
designed and conducted to produce
information to inform delivery of best care.
An audit asks ‘Does this service reach a
pre-determined standard?’
A service evaluation programme considers
a service’s effectiveness or efficiency
through systematic assessment of its
aims, objectives, activities, outputs,
outcomes and costs.
This approach can be used to compare a
new treatment or service with an existing
one, but cannot be used for measurement
against standards. Service evaluation
generates evidence of the effectiveness of
a service which may lead to service
redesign and is fully endorsed by the NHS
(18, 19).
Using a service evaluation approach
usually does not require a sponsoring
organisation, R&D approval or ethics
approval and avoids the judgemental
nature of an audit. Unlike clinical
research, this approach does not allow
generalisability of the result, but the aims
of the project, the selection of the centres
and the parameters of the data collection
can be adapted to ensure that the
conclusions are clear and persuasive (20).
Scoping Phase
This begins with the assembly of an expert
group which consists of doctors, other
healthcare professionals (HCPs),
pharmaceutical company representatives,
and commissioners. Group and one-to-
one meetings are vital in determining the
aims and scope of the project and its
viability. Assumptions are developed and
agreed at this stage.
The group can select a practical primary
outcome, with test results and other
disease indicators as secondary
Scoping Phase
1-to-1 Meetings
Identification & Recruitment
of Expert Group
Collection Parameters
Go or No-
Go Decision
Development Phase
Online Database &
Development Sign-Off
Data Collection Phase
Ongoing Centre
Managment
Real-Time Analysis
Review Phase
Expert Group Reconvenes
In-Depth Analysis
Feedback
Future Actions Planned
Outcomes
Journals
National & International
Conferences
Re-submissions to
NICE/SMC
Service Redesign
THE METHODOLOGY OF A CUSTOM DATA COLLECTION PROGRAMME

7
outcomes. This is of particular interest to
commissioning groups and other payors.
Practical Outcomes:
 Total patient-NHS interactions
 Number of procedures
 Number of outpatient appointments
 Number of bed days
 Emergency department visits.
Development Phase
The online data entry tool must be
standardised across centres and tested.
The internal logic can be designed to
check for incomplete fields and
inconsistency, validating the data.
Intelligent design at this stage greatly
shortens the collection and analysis
phase.
Data collection phase
Patient data can be entered quickly
following online training sessions with
each of the participating centres. Real-
time analysis allows monitoring of the
process and centres can visualise their
outcomes.
This online data entry tool will allow HCPs
across selected centres enter data
retrospectively from patient records.
 Data can be prospectively entered
as it becomes available
 Data entry validated and real-time
analysis offered.
 Only necessary data is input, and
data is pseudonymised
Review Phase
The expert group reconvenes and the
findings across the centres are presented
and discussed. The objectives of the
programme and the data collected
determines the next steps.
Outcomes
RWE generated using this methodology
has a proven record of contributing to
meaningful outcomes (21, 22):
 Abstract presentation at national and
international conferences
 Publications
 Successful re-submission to
advisory bodies e.g. Scottish
Medicines Consortium (SMC)
 Presentations to specialist
commissioning groups
 Case Studies
CONTACT US Real World Evidence
 KTL Influence Mapping
 Healthcare Data Analytics
 Patient Experience

8
Destination: RWE
Each of the three routes leads to RWE, providing
data which can drive clinical outcomes and
enhance the value of a product. However you will
see that each route has strengths and weaknesses,
did you make the right choice?
Big Data is important in identifying trends and
relationships and inspiring new directions for
marketing and growth. The scope and volume of
data can be persuasive and powerful, however
accessibility and completeness varies.
Pragmatic RCTs and prospective observational studies require a considerable investment in
time and money but can produce robust and powerful RWE.
Custom data collection, via the approach outlined, allows data to be collected, analysed and
presented within a period of several months. This is invaluable when faced with an
approaching deadline, for instance, submissions or re-submissions to NICE, SMC or
specialist commissioners. This data can differentiate a product in a competitive marketplace
and the data collection process encourages engagement with the centres and key
prescribers.
The table compares the key attributes of these RWE data sources in addition to the
traditional RCT. There is no single way to collect RWE, and each of the routes offer benefits
which may align with your objectives. Of the three routes, custom data collection offers an
excellent balance: it is a low-cost and expedient programme, enabling both retrospective and
prospective data collection and offering the bonus of HCP engagement. Adding this
programme to your strategy can bridge the gaps in clinical trial data and offers a clear path
to success.
NEXT ON THE RWE SERIES JOIN US FOR PART III IN WHICH WE LOOK AT RWE DATA
ANALYSIS. VISIT SVMPHARMA.COM & FOLLOW US @SVMPHARMA
Retrospective Prospective On-
label
Off-
label
HCP
Engagement
Cost Speed Level of
Evidence
RCT Low High Slow High
RWE Trial Low High Slow Medium-
High
Big Data Low Medium Medium Medium
Custom Data
Collection
High Low Fast Medium
A COMPARISON OF RWE DATA SOURCES ALONGSIDE THE TRADITIONAL RCT
WHICH ROUTE WILL YOU TAKE FOR YOUR NEXT REAL WORLD EVIDENCE PROJECT?

9
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application of big data to health care. JAMA.
2013;309(13):1351-2.
2. Bollier D, Firestone CM. The promise and
peril of big data: Aspen Institute, Communications
and Society Program Washington, DC, USA; 2010.
3. Williams T, van Staa T, Puri S, Eaton S.
Recent advances in the utility and use of the
General Practice Research Database as an
example of a UK Primary Care Data resource.
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2012;3(2):89-99.
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AD, Kalra D, Hingorani AD, et al. Data Resource
Profile: Cardiovascular disease research using
linked bespoke studies and electronic health
records (CALIBER). International Journal of
Epidemiology. 2012.
5. Rowlingson B, Lawson E, Taylor B, Diggle
PJ. Mapping English GP prescribing data: a tool for
monitoring health-service inequalities. BMJ Open.
2013;3(1).
6. Keen J, Calinescu R, Paige R, Rooksby J,
editors. Big Health Data: Institutional and
Technological Challenges. paper at IPP2012
conference ‘Big Data, Big Challenges’, Oxford,
Sept; 2012.
7. Schultz T. Turning healthcare challenges
into big data opportunities: A use-case review
across the pharmaceutical development lifecycle.
Bulletin of the American Society for Information
Science and Technology. 2013;39(5):34-40.
8. Pope C, Halford S, Tinati R, Weal M.
What's the big fuss about 'big data'? Journal of
health services research & policy. 2014;19(2):67-8.
9. Square T, Lane B. Care. data. 2013.
10. Mitchell C, Moraia LB, Kaye J. Health
database: Restore public trust in care. data project.
Nature. 2014;508(7497):458-.
11. Benson K, Hartz AJ. A Comparison of
Observational Studies and Randomized, Controlled
Trials. New England Journal of Medicine.
2000;342(25):1878-86.
12. Black N. Why we need observational
studies to evaluate the effectiveness of health
care1996 1996-05-11 07:00:00. 1215-8 p.
13. Silverman SL. From Randomized
Controlled Trials to Observational Studies. The
American Journal of Medicine. 2009;122(2):114-20.
14. Brass E. The gap between clinical trials
and clinical practice: the use of pragmatic clinical
trials to inform regulatory decision making. Clinical
pharmacology and therapeutics. 2010;87(3):351-5.
15. Patsopoulos NA. A pragmatic view on
pragmatic trials. Dialogues in Clinical Neuroscience.
2011;13(2):217-24.
16. Roland M, Torgerson DJ. Understanding
controlled trials: What are pragmatic trials? BMJ
(Clinical research ed). 1998;316(7127):285.
17. New JP, Bakerly ND, Leather D,
Woodcock A. Obtaining real-world evidence: the
Salford Lung Study. Thorax. 2014.
18. University_Hospitals_Bristol. Operational
Guidance 2007. Available from:
http://www.uhbristol.nhs.uk/media/1689453/agreed_
definitions_of_rd_ca_se_v4_oct_07_pdf.pdf.
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Defining Research 2010. Available from:
http://www.uhbristol.nhs.uk/media/1572809/defining
_research_leaflet_1_.pdf.
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practice development and service evaluation:
Implications for research and clinical governance.
Practice Development in Health Care. 2005;4(1):33-
9.
21. Plant BJ, Downey D, Eustace JA,
Gunaratnam C, Haworth C, Jones A, et al. WS7.4
Inhaled aztreonam lysine (Cayston) therapy
significantly improves lung function, weight,
hospitalisations and excerbation rates prospectively
– an Irish and UK real world experience. Journal of
Cystic Fibrosis.13:S15.
22. Scottish_Medicines_Consortium. Re-
submission of aztreonam lysine (Cayston)2015 12
January 2015. Available from:
http://www.scottishmedicines.org.uk/SMC_Advice/A
dvice/753_12_aztreonam_Cayston/aztreonam_lysin
e_Cayston_RESUBMISSION.

SVMPharma Real World Evidence – The RWE Series – Part II: Collecting Real World Evidence

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