CORBEL Data Sharing webinar slides

Data Sharing and Reuse from ClinicalTrials:
Principles and Recommendations
Presenters: Christian Ohmann (ECRIN-ERIC), Rita Banzi (Mario Negri), Steve
Canham (ECRIN-ERIC)
Host:Vera Matser (EMBL-EBI)
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CORBEL Webinar Series
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This webinar is being recorded
27/02/2018Data Sharing and Reuse from Clinical Trials: Principles and Recommendations

AUDIENCE Q&A SESSION
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BACKGROUND
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Since 2015, thirteen ESFRI Research Infrastructures from the field
of BioMedical Science (BMS RI) joined their scientific capabilities
and services to transform the understanding of biological
mechanisms and accelerate its translation into medical care.
• biobanking & biomolecular
resources
• curated databases
• marine model organisms
• systems biology
• translational research
• functional genomics
• screening & medicinal
chemistry
• microorganisms
• clinical trials
• structural biology
• biological/medical imaging• plant phenotyping
• highly pathogenic
microorganisms
27/02/2018

CORBEL MISSION
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Modern biological and biomedical research involves complex
projects and a variety of different technologies.
Some of the most important discoveries are made at the
interface between different disciplines.
CORBEL will harmonise access and services for complex
research projects involving more than one RI that offer:
• biological and medical technologies
• biological samples and
• data services

TODAY’S PRESENTERS
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Christian Ohmann, Mathematician by training, he
received the habilitation inTheoretical Surgery and
has over 30 years of experience in clinical trials and
clinical research informatics, as Head of the
Theoretical Surgery Unit at the Heinrich-Heine-
University of Düsseldorf first and then as Scientific
Head of the Coordination Centre for ClinicalTrials
in the same University, until 2014.
Retired, he is now working for the European Clinical Research
Infrastructures Network (ECRIN) as a chair of Network Committee and
chair of Independent Certification Board; he also represents ECRIN in
several H2020 funded projects such as CORBEL.

27/02/2018Data Sharing and Reuse from Clinical Trials: Principles and Recommendations 7
Rita Banzi is the Coordinator of the Centre for
Drug Regulatory Policies at the Mario Negri
Institute in Milan. She received her BS in
Pharmaceutical Chemistry from the University of
Bologna and completed her PhD in
Pharmaceutical Sciences at the same University.
She’s been working at Mario Negri since 2007 as staff of the Italian
Cochrane Centre for the development of systematic reviews and
ECRIN European Correspondent for Italy.
She has an extensive experience and expertise in clinical research,
systematic review methodology and appraisal of mechanisms of
marketing approvals of drugs in Europe.

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Steven Canham, IT consultant specializing in
clinical trial data systems and data
management, with 16 years' experience with
clinical research systems. From 2002 - 2011 he
was the Head of the IT team at the trials unit at
the Institute of Cancer Research, London.
He is currently Secretary to the ECRIN Independent Certification Board
for data centres, Chair of ECRIN standards review, ECRIN auditor and
member of the UK's international Registration Panel for clinical trials
units.
Steve is also involved in several H2020 projects on behalf of ECRIN.

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Waste in research
Efficiency of knowledge generation
Transparency/open science/FAIR
CLINICALTRIAL DATA SHARING AND REUSE
CULTURAL CHANGE

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STAKEHOLDER ENGAGEMENT

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• reduce unnecessary duplication of studies and costs of future
studies
• reduce exposure of participants in future trials to avoidable harms
• lead to new hypothesis about mechanisms of disease and more
effective therapies to be tested in additional research
• better evidence on the safety and effectiveness of therapies for
patients
• better evidence allows health care professionals and patients to
more informed decisions about health clinical care
POTENTIAL BENEFIT*
*IOM. 2015: Sharing clinical trial data: Maximizing benefis, minimizing risk.
Washington, DC. National Academics Press

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• participant privacy
• unfair commercial use
• invalid secondary analyses
• no credit for clinical trialists/sponsors
• other risks
*IOM. 2015: Sharing clinical trial data: Maximizing benefis, minimizing risk.
Washington, DC. National Academics Press
POTENTIAL RISKS*

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• MRC, UKCRC, CRU, Wellcome: Good practice principles for sharing
IPD from publicly funded clinical trials (Tudor Smith et al., April 2015)
• MRCT, Wellcome, Arnold: Launch of data sharing working groups
2015 (based on workshop, Harvard, 30-31, March 2015)
• Wellcome: Assessing the research potential of access to clinical trial
data.A report to the Wellcome Trust (Varnai et al.,Technopolis Group,
2014)
• Institute of Medicine: Sharing clinical trial data: maximizing benefts,
minimizing risk (IOM, 2015)
• NordicTrial Alliance Group: Transparency and registration in Clinical
Research in the Nordic Countries (2015)
RECOMMENDATIONS AND POLICIES

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WHAT IS AVAILABLE ANDWHAT IS NEEDED
• What is available
Open and published policies and recommendations embedded in
- geographical/national context (e.g. US, UK, Nordic countries)
- centred on a specific stakeholder group (e.g. pharmaceutical industry)
- centred on a specific subset of clinical trial data (e.g. ICMJE – published
data)
• What is needed
Up to date, precise, broadly based and workable recommendations
supporting data sharing from clinical trials with
- focus on non-commercial trials,
- European origin
- perspective of the researcher

Meta-analysis
Trial methodology
Univ. Copenhagen (C. Gluud)
Univ. Liverpool (C.Tudor Smith)
Transparency/registration
WHO ICTRP (G. Karam)
Ottawa Group (K. Krleza-Jeric)
EQUATOR (P. Ravaud)
MRCT Harvard (B. Bierer)
Funders
NCI (J. Galvez)
UK MRC (R. Knowles)
NHMRC (D. Ghersi)
Wellcome (J. O‘Callaghan)
ECRIN
(S. Battaglia, C. Kubiak, M. Matei,
J. Demotes, C. Ohmann)
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MULTI-STAKEHOLDERTASKFORCE
Publishers
BMJ (T. Groves)
BMC (D. Shanahan; H. Faure)
Regulatory bodies
EMA (M. Dias)
Patient organisations
EATG (P. Marques)
Ethics
Univ.Vienna (C. Druml)
Sanger (S. Bowers/C. Smee)
Data protection
MedConsult (E. vanVeen)
TMF (I. Schluender)
IT-experts
ECRIN (S. Canham)
EBI (D. Spalding)
EUDAT (A. Lukkarinen, C. Ariyo)
Univ. Düsseldorf (W. Kuchinke)
Mario Negri (R. Banzi, L. Clivio)
Standardisation bodies
CDISC (P. Houston, R. Kush,
L. Becnel, P. van Reusel)
Glob. All. Gen.Health (J. Wilson)
EHR4CR (D. Kalra)
DataCite (M. Fenner)
IT-/service provider
Medidata/TrialGrid (A. Newbigging)
Trial Data Solutions (G. Rienk)
Consensus expert
Univ. Marburg (H. Sitter)

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CONSENSUS PROCESS
Aim: Development of a consensus document on providing access
to individual participant data from clinical trials by a multi-
stakeholder taskforce
Method: Group Nominal Process with three workshops and
written feedback
- 4 March 2016 (Paris), 6-7 October 2016 (Milano),
- 27-28 March 2017 (Paris)
Results: Consensus document with 10 principles and 50
recommendations

CORBEL CONSENSUS PROCESS OUTPUT
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Promotion,
incentives, resources
Explicit broad
consent to sharing
and re-use
De-identification
pseudo-
anonymised data
Data structured,
described and formatted
in standard ways to
promote inter-operability
 As open as possible, as closed as
necessary to protect privacy
and reduce misuse
 Explicit, reproducible, and
transparent process
 Access to data and
data objects
 Data repositories
Metadata to promote
discoverability

P1: MAKE DATA SHARING A REALITY
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The provision of individual-participant data should be promoted,
incentivised and resourced so that it becomes the norm in clinical
research. Plans for data sharing should be described prospectively,
and be part of study development from the earliest stages.
Culture and environment

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Different
stakeholders
recognize
data sharing
as good
practice
Data
stewardship
and data
ownership
Institutional
policies to
promote
data sharing
Data sharing
prospectively
planned
Standards
and template
Academic
and societal
rewards and
incentives
for data
sharing
Systems to
monitor
compliance
to plans
Financial
models to
support data
preparation,
storage and
sharing
Discoverabili
ty and
Citability of
datasets
P1: MAKE DATA SHARING A REALITY

P2: CONSENT FOR DATA SHARING
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Individual-participant data sharing should be based on explicit
broad consent by trial participants to the sharing and reuse of their
data for scientific purposes.
Information and awareness

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Explicit
consent to
share and re-
use of data Consent to
sharing data/
consent to
participation:
separate but
concurrent
Consent as
broad as
possible (for
scientific
purposes)
Information
on reasons
for sharing
and benefit Right to
withdrawn,
practical
difficulties
explained
Information
on data
preparation,
storage and
access
mechanisms Clear and
concise
language
P2: CONSENT FOR DATA SHARING

P5, 6, 7: RIGHTS,TYPES, AND MANAGEMENT
OF ACCESS
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• P5: Access to individual-participant data and trial documents should
be as open as possible and as closed as necessary, to protect participant
privacy and reduce the risk of data misuse.
• P6: In the context of managed access, any citizen or group that has
both a reasonable scientific question and the expertise to answer that
question should be able to request access to individual-participant data
and trial documents.
• P7:The processing of data access requests should be explicit,
reproducible, and transparent but, so far as possible, should minimise the
additional bureaucratic burden on all concerned.
Governance of access

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Range of
access types
(no one-size-
fit all)
Compliance
with basic
rules to
ensure a fair
data sharing
Board
overseeing
data sharing
Right to
request data
not limited to
specific
professions
or roles
Friendly
interfaces to
request and
access to
data
Transparency
of methods
and results of
secondary
analysis
Data use
agreement
P5, 6, 7: RIGHTS,TYPES, AND MANAGEMENT
Data
generator
and data
users may
collaborate
(or not)
Metrics of
access and
reuse of data

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P3: PROTECTION OFTRIAL PARTICIPANTS
IPD made available for sharing should be prepared for
that purpose, with de-identification of data sets to
minimise the risk of reidentification.The de-identification
steps that are applied should be recorded.

P3: PROTECTION OFTRIAL PARTICIPANTS
Before being
shared, data
should be de-
identified
Shared data
should be
pseudonymous
where possible
Attempted
reidentification
should be
forbidden
Standard
techniques to
be applied &
documented
Residual risks
should be
assessed &
documented
No consent:
data sharing
*may*
be possible
Services to
support data
preparation

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P4: DATA STANDARDS
To promote interoperability and retain meaning within
interpretation and analysis, shared data should, as far
as possible, be structured, described and formatted
using widely recognised data and metadata standards.

P4: DATA STANDARDS
Data and
coding
standards
built in from
the outset
Start with the
CDISC
standards
Descriptive
metadata
required for all
datasets
Become
familiar with
data
standards
Help to
develop data
standards
Use
standardised
file formats

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P8, 9: DATA MANAGEMENT AND
REPOSITORIES
• P8: Besides the IPD data sets, other clinical trial data objects
should be made available for sharing (e.g., protocols, clinical
study reports, statistical analysis plans, blank consent forms) to
allow a full understanding of any data set.
• P9: Data and trial documents made available for sharing
should be transferred to a suitable data repository to help
ensure that the data objects are properly prepared, are
available in the longer term, are stored securely and are subject
to rigorous governance.

P8, 9: DATA MANAGEMENT AND
REPOSITORIES
Repositories
should be
compliant
with quality
criteria
Information on
different
repositories
made available
Temporary
import of data
from different
sources
Formal data
transfer
agreements
needed
Analysis
environments
required?
Facilities to
analyse and
document

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P10: DISCOVERABILITY AND METADATA
Any data set or document made available for sharing
should be associated with concise, publicly available and
consistently structured discovery metadata, describing
not just the data object itself but also how it can be
accessed.This is to maximise its discoverability by both
humans and machines.

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P10: DISCOVERABILITY AND METADATA
We need a
metadata
schema for
discoverability
Persuade
repositories to
use it or a
mappable
system
Tools required
to harvest the
metadata
Identifiers
(DOIs) need
to be applied
to all data
objects
Tools
required to
help apply
the metadata
A metadata
repository,
single portal
access
Long term
sustainability
required

THE PAPER
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doi:10.1136/bmjopen-2017-018647

THE NEXT STEPS
Under discussion, but should probably include…
Providing general
background material
Developing example
documents, e.g. data use
agreements
Develop a metadata
repository (or at least
a pilot)
Provide resource locator
services (e.g. for data
standards)
Develop
repository
standards
List available
repositories
Establish a metadata
schema
Developing example
processes, e.g. SOPs on
data sharing
Develop IT tools, e.g. to
assign metadata
Try to ensure full
citation of data use
Establish services, e.g. for de-
identification
Collect example
templates, checklists

Questions?
Data Sharing and Reuse from ClinicalTrials: Principles and
Recommendations
Presenters: Christian Ohmann (ECRIN-ERIC), Rita Banzi
(Mario Negri), Steve Canham (ECRIN-ERIC)
Host:Vera Matser (EMBL-EBI)
14/05/2018footer 34

NEXT WEBINAR
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Title: Single sign-on for Life Science services
Speakers: Mikael Linden, Kostas Koumantaros
Date:Tue 17th April, 2018
Time: 14:30 CET
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