Ref. Ares(2018)3463667 - 29/06/2018
D11.2: Data Management Plan
Dissemination level: Public
Document type: Report
Version: 1.0.0
Date: 29 June, 2018
This project has received funding from the European Union's Horizon 2020
research and innovation programme under Grant Agreement #779306. This
result only reflects the author's view and the EU is not responsible for any use
that may be made of the information it contains.
Document Details
Project Number
Project Title
Project Acronym
Title of deliverable
Work package
Project start date
Due date of deliverable
Actual date of delivery
Author(s)
Other contributing partners
779306
COMED Pushing the boundaries of the cost
and outcome analysis of medical technologies
COMED
Data Management Plan
WP11
1 January 2018
30 June 2018
29 June 2018
Aleksandra Torbica, Benedetta Pongiglione
(Università Bocconi (UB))
Universitaet Hamburg (UHAM), the University
of Exeter (UNEXE), Universitaet Bern (UBERN),
Erasmus Universiteit Rotterdam (EUR), Syreon
Kutato Intezet Korlatolf Felelossegu Tarsasag
(SYREON),
Table of Contents
EXECUTIVE SUMMARY ................................................................................................................................ 4
1. INTRODUCTION .................................................................................................................................. 5
1.1. Why is a DMP needed? .......................................................................................................... 5
1.2. Implementation of the DMP in COMED ................................................................................ 5
1.3. What kind of data are considered in the DMP? .................................................................... 6
2. COMED PROJECT ............................................................................................................................. 7
2.1. Project’s objectives ................................................................................................................ 7
2.2. Project’s data......................................................................................................................... 8
3. FAIR DATA ....................................................................................................................................... 9
3.1. Findability ............................................................................................................................ 10
3.2. Accessibility ......................................................................................................................... 10
3.3. Interoperability .................................................................................................................... 11
3.4. Re-Usability.......................................................................................................................... 11
4. DATA MANAGEMENT PER WP ........................................................................................................... 11
4.1. WP1: Real-world evidence for economic evaluation of medical devices ........................... 11
4.1.1. Task 1 ........................................................................................................................... 12
4.1.2. Task 2 ........................................................................................................................... 13
4.2. WP2: Use of surrogate outcomes for medical devices: advanced methodological issues . 14
4.2.1. Task2 ............................................................................................................................ 14
4.2.2. Task3 ............................................................................................................................ 16
4.3. WP3: Outcome measurements and Patient Reported Outcome Measures assessment for
mHealth .......................................................................................................................................... 17
4.3.1. Task 1 ........................................................................................................................... 17
4.4. WP4 & WP5: Variation in the use of medical devices......................................................... 18
4.5. WP6: Early dialogue and early assessment of medical devices .......................................... 20
4.5.1. Task 2 ........................................................................................................................... 20
4.5.2. Task 3 ........................................................................................................................... 21
4.6. WP7: Coverage with evidence development for medical devices ...................................... 22
4.6.1. Task 3 ........................................................................................................................... 22
4.7. WP8: Transferability of medical device HTA/EE and of evidence on uncertainty factors
across EU Member States .............................................................................................................. 23
4.7.1. Task 1 ........................................................................................................................... 23
4.7.2. Task 2 ........................................................................................................................... 25
4.7.3. Task 3 ........................................................................................................................... 26
EXECUTIVE SUMMARY
In December 2013, the European Commission has launched a flexible pilot for open access to
research data (ORD pilot), as part of the Horizon 2020 Research and Innovation Programme. The
aim of the ORD pilot is to disseminate results of publicly-funded research more broadly and faster,
for the benefit of researchers, innovative industry and citizens.
As part of H2020, COMED is committed to open its research data through the ORD pilot, and the
Data Management Plan (DMP) is one of the instrument to achieve this objective.
COMED’s DMP is one of COMED’s deliverables and gives an overview of available research data,
access and the data management and terms of use. The Deliverable outlines how the research data
collected or generated will be handled during and after COMED action, describes which standards
and methodology for data collection and generation will be followed, and whether and how data
will be shared. The DMP is intended as a living document and reflects the current state of the
discussions, plans and ambitions of the COMED partners, and will be updated as work progresses.
This document follows the template provided by the European Commission in the Participant Portal.
This deliverable provides the first version of the DMP elaborated by the COMED project and has
been produced jointly by all members of COMED consortium.
1.
INTRODUCTION
1.1. Why is a DMP needed?
In December 2013, the Commission has launched a flexible pilot for open access to research data
(ORD pilot), as part of the Horizon 2020 Research and Innovation Programme. The pilot aims to
improve and maximise access to and re-use of research data generated by Horizon 2020 projects,
taking into account the need to balance openness and protection of scientific information,
following the principle of 'as open as possible, as closed as necessary'.
The rationale behind the choice of committing to open data through the ORD pilot is to disseminate
results of publicly-funded research more broadly and faster, for the benefit of researchers,
innovative industry and citizens.
Open Access allows accelerating dissemination process as well as research results to reach the
market, but also avoids a duplication of research efforts. Open Access policy is also beneficial to
researchers. Making the research publicly available increases the visibility of the performed
research, as well as foster collaboration potential with other institutions in new projects. It also
eases reproducibility of results, pushing in the direction of the current debates among the scientific
community 1.
Projects must aim to deposit the research data needed to validate the results presented in the
deposited scientific publications, known as "underlying data". In order to effectively supply this
data, projects need to consider at an early stage how they are going to manage and share the data
they create or generate. The Data Management Plan (DMP) specifies the implementation of the
pilot, in particular with regard to the data generated and collected, the standards in use, the
workflow to make the data accessible for use, reuse and verification by the community and define
the strategy of curation and preservation of the data.
1.2. Implementation of the DMP in COMED
The partners of COMED participate in the Open Access Pilot for Research Data. DMP is included in
the Description of Work (DoW) as a deliverable (D11.2). This DMP is a living document, and the
creation of an initial version is scheduled for project-month 6. It is drafted in compliance with the
guidelines given on data management in the Horizon 2020 Online Manual 2. This deliverable will
evolve during the lifetime of the project and represent faithfully the status of the project
1
See for example McNutt M. Reproducibility. Science. 2014 Jan 17;343(6168):229. doi:10.1126/science.1250475.
Peng RD. Reproducible research and Biostatistics. Biostatistics. 2009 Jul; 10(3):405-8. doi: 10.1093/biostatistics/kxp014.
2
http://ec.europa.eu/research/participants/docs/h2020-funding-guide/cross-cutting-issues/open-access-datamanagement/data-management_en.htm
reflections on data management. Updates of the DMP are thus planned and will be submitted to
the EC as an integral part of the Project Periodic Reports.
Lead for this task will be with UB, though all partners are involved in the compliance of the DMP.
The partners agree to deliver datasets and metadata produced or collected in COMED according to
the rules described in the DMP, and contribute to the document for the part relative to the working
package (WP) of which they are leader (section 4). The project office and in particular the Scientific
Officer are also central players in the implementation of the DMP and will track the compliance of
the rules agreed.
1.3. What kind of data are considered in the DMP?
In the last updated version of the Guidelines to the Rules on Open Access to Scientific Publications
and Open Access to Research Data in Horizon 2020 3, it is stated that research data refers to
information, in particular facts or numbers, collected to be examined and considered as a basis for
reasoning, discussion, or calculation. In a research context, examples of data include statistics,
results of experiments, measurements, observations resulting from fieldwork, survey results,
interview recordings and images. The focus is on research data that is available in digital form. Users
can normally access, mine, exploit, reproduce and disseminate openly accessible research data free
of charge.
The Open Research Data Pilot applies to two types of data:
1.
the 'underlying data' (the data needed to validate the results presented in scientific
publications), including the associated metadata (i.e. metadata describing the research data
deposited), as soon as possible
2.
any other data (for instance curated data not directly attributable to a publication, or raw
data), including the associated metadata, as specified and within the deadlines laid down in
the DMP – that is, according to the individual judgement by each project/grantee.
To the purposes of the DMP as a deliverable of the COMED project, we will distinguish collected research
data and generated research data. The former are meant as existing data produced by various sources,
which will be systematically collected and stored together in data libraries/platforms. We will
produce metadata for this type of data, describing the availability of the datasets included in the
library/platform that will be created. Generated data are those data that will be created ex novo as
part of the project. Different data pose different challenges to accomplish open data through the
ORD pilot.
3
file:///C:/Users/Bebe/Documents/BEBE/1)%20CERGAS/COMED/Data%20Management%20Plan/h2020-hi-oa-pilotguide_en.pdf
2.
COMED PROJECT
2.1. Project’s objectives
The overarching objective of the COMED project is to push the boundaries of existing methods for
cost and outcome analysis of healthcare technologies, and to develop tools to foster the use of
economic evaluation in policymaking. Within this general agenda, the COMED project explores a
very broad range of healthcare technologies that are classified under one specific category: medical
devices.
The main objectives of COMED are:
1.
2.
3.
to improve economic evaluation methods for medical devices in the context of the health
technology assessment framework by increasing their methodological quality and integrating
data from different data sources
to investigate health system performance through analysis of variation in costs and outcomes
across different geographical areas
to strengthen the use of economic evaluation of medical devices in policy making
The integration of (existing) data from different data sources as well as the generation of ad hoc
new data are the key vehicles to achieve the objectives of the COMED’s project. Such data will also
be used and of use after the end of the project, not only for the member of the consortium but also
for other stakeholders and researchers.
A complete list of the data that will be collected and created with the corresponding timetable and
leading partner is shown in Table 1 for each WP and relevant task.
WP
Task
1
1
2
2
2
Table 1. COMED Data
Data
Timelines
Dataset name
collected/generated
RWD Mapping
Expert solicitation
Learning Curve
Surrogate Outcome
Mapping
PI
Collected
M1-M12
UB
Generated
M6-M12
EUR
Collected
M6-30
UEMS
3
3
Semi-structured
interviews on Surrogate
Outcomes
Generated
M18-30
UEMS
1
mHealth Mapping
Collected
M6-12
UB
Data library
Collected
M6-M18
HCHE
Expert survey Early
Dialogue
Generated
M6-M18
UBERN
M6-M24
UBERN
4&5
6
2
3
7
8
Case Study Early Dialogue Collected/Generated
1
Interviews
Generated
M24-M30
UB
1
RWE on Transferability of
MD HTA/EE
Collected
M6-M12
SYREON
2
Focus group
Generated
M18-24
SYREON
3
Stakeholders miniconference
Generated
M18-M33
SYREON
2.2. Project’s data
As said, COMED will both collect existing data from partners and third parties, and will create new
data. Research data will be collected/generated and metadata produced; the project will also
produce reviews, manuscripts and dissemination material. While the aim of this DMP is to explain
and describe Research Data and Metadata according to the H2020 framework, in the following we
briefly outline all different output that will be originated in this project.
•
•
Research data: this category comprehends on the one hand, existing source of data including databases; surveys; patient chart reviews; randomized controlled trials; pragmatic
clinical trials; observational data from cohort studies; registries; routine administrative
databases; etc.- that will be mapped and structured as data library and made available with
metadata, according to open access rules of each data. On the other hand, generated
research data will consist on various forms such as surveys, structured and semi-structured
interviews, focus group, discrete choice experiments, and others. These data will be created
to address specific objectives of different working packages and will be produced with
respective metadata.
Metadata: refers to “data about data”, it is the information that describes the data that is
being published with sufficient context or instructions to be intelligible for other users.
•
•
•
Metadata will allow a proper organization, search and access to the generated information
and will enable to identify and locate the data via a web browser or web based catalogue. In
the context of data management, metadata will form a subset of data documentation, either
collected or generated, that will explain the purpose, origin, description, time reference,
creator, access conditions and terms of use of a data collection.
Reviews: reviews, where possible systematic, will be the starting point of most WPs. They
will synthesize all the key findings in the current literature on specific topics and investigate
contributions from a broad range of scientific disciplines. In the reviews existing works are
synthesized and elaborated in a new (generated) piece of evidence. The present DMP does
not consider literature reviews among the COMED datasets. All reviews published within the
COMED project will be open access.
Manuscripts: manuscripts will consist of all the reports and peer reviewed articles generated
during the project, all deliverables, publications and internal documents. Microsoft Word
(DOCX) and PDF will be used for final versions, while intermediate versions can consider the
usage of alternative software, such as ODT or TEX (LateX) files.
Dissemination material: COMED will produce dissemination material in a diversity of forms:
website, project meetings, workshops, flyers, public presentations in national and
international conferences.
All partners will be actively involved in the production of each type of data.
3.
FAIR DATA
This DMP contains information on Research data and metadata and is conceived as a living
document. At this stage in the research, as shown in Table 1, most of data collection and generation
have still to be conducted, and a lot of questions concerning the data are open for discussion, mostly
concerning the FAIR principles (Findable, Accessible, Interoperable, Re-use). We will add relevant
information to the DMP over the course of the project. An intermediate and final version will be
issued before the end of the project, and additional editions will be produced if needed.
To compose this DMP, the work package leaders have been asked to describe the different datasets
that will be collected/generated within their WP. However, since many sections can only be
provisionally filled in, and some general guidelines apply to all datasets, in this section we report
the common FAIR principles and general rules that will be followed to collect, generate and manage
the data during the project.
For data generation, we will follow the EC guidelines for ethics self-assessment 4, to inform individual
research subjects about all aspects of the research in which they are being asked to participate,
4
http://ec.europa.eu/research/participants/data/ref/h2020/grants_manual/hi/ethics/h2020_hi_ethics-selfassess_en.pdf
including the future use of the data they might provide, the complete details and possible dangers
they might face. We will inform that participation is entirely voluntary and document participants’
informed consent in advance, unless national law provides for an exception (e.g. in the public
interest). The informed consent will be delivered making sure that participants can fully understand;
it will illustrate clearly the aims, methods and implications of the research, the nature of the
participation and any benefits, risks or discomfort that might ensue. We will seek participants’
consent in written form whenever possible (e.g. by signing the informed consent form and
information sheets). For data collection, we will comply with the General Data Protection Regulation
(GDPR) (EU) 2016/679 which has come into force in May 2018.
In Section 4, a specific description of each dataset is presented separately for work package tasks,
using the standard EC template for a DMP and including only dataset-specific elements, while FAIR
principles applying to all datasets are described below in section 3.1.
3.1. Findability
All sources of data collected and generated will be complemented by metadata.
Each generated dataset will get a unique Digital Object Identifier (DOI).
For the naming of each dataset, files and folders at data repositories will be versioned and
structured by using a name convention consisting of project name, working package, dataset name
and version (e.g. COMED_WP1_DS_1.xlsx)
Keywords will be added in line with the content of the datasets and with terminology used in the
specific scientific fields to make the datasets findable for different researchers.
Being COMED a multidisciplinary project, we will use metadata standards for General Research
Data, such as Data Package; if a dataset pertains to specific discipline only, metadata standards for
the specific discipline will be used.
3.2. Accessibility
As described before, our intention is to keep open as many data sets as possible. This will be
balanced with the respect of the principle of protection of personal data, according to which
everyone has the right to the protection of personal data concerning him or her and to access to
data which has been collected concerning him or her, and the right to have it rectified 5. Therefore,
there might be circumstances under which open access to the data will not be possible. This can
occur if we cannot guarantee the privacy of the participants, if the collected datasets are not open
access, etc. In principle, if open access is not possible, we will try to make the dataset open under a
restricted license, and in the last instance, if no other option is possible, we will keep the dataset
5
This right is enshrined in article 8 of the EU Charter of Fundamental Rights.
completely closed and justify why this is needed. Accessibility to datasets will be decided in
agreement with members of the COMED consortium.
All open datasets will be stored in a trusted repository. Possible repositories are: Registry of Open
Access Repositories (ROAR); Directory of Open Access Repositories (OpenDOAR).
3.3. Interoperability
Interoperability means allowing data exchange and re-use between researchers, institutions,
organisations, countries. Hence, whenever possible we will adhere to standards for formats and
issue data and metadata in available (open) software applications.
Data will be shared in cloud only when this is allowed. When this will occur, either for internal use
or involving external stakeholders, data will be anonymized and personal information will be
protected.
3.4. Re-Usability
The datasets will be licensed under an Open Access license, whenever possible. However this will
depend on the level of personal data protection, and the Intellectual Property Right (IPR) involved
in the data set.
Our intention is to make data re-usable for third parties as much as possible and for the longest
possible period. If a period of embargo will be necessary (e.g. if a dataset contains specific IPR or
due to time to publish), we will specify why and for how long. The length of time that the datasets
will be stored will depend on their content. For example if the dataset contains medical devices that
we foresee will be replaced soon, these may not be stored indefinitely.
4. DATA MANAGEMENT PER WP
In this section, datasets expected to be collected and/or generated as part of the WPs of the COMED
project are presented. The development and management of each dataset will be inspired by and
follow the general FAIR principles and procedures described in previous section, which will act as
boundaries and guidelines for the generation of new data and collection of existing sources. As the
DMP is a living document, more details will be provided in the future versions. Here we commit to
apply the FAIR principles to datasets whenever possible.
4.1. WP1: Real-world evidence for economic evaluation of medical devices
4.1.1. Task 1
Section 1: Data summary
The purpose of the data collection WP1 task 1 is to provide a comprehensive assessment of possible
sources of real world evidence for medical devices in EU countries.
Therefore we will collect and then use existing data.
The possible sources of RWD that will be explored are: Databases; Surveys; Patient chart reviews;
Pragmatic clinical trials; Observational data from cohort studies; Registries.
Sources of RWD can be collected also at the sub-national or cross-national level by scientific
networks (e.g. scientific societies, hospital networks, etc.).
The data will be useful for other project partners and in the future for other research groups; and
indirectly for policymakers, that will be able to apply methods to initiate structured collection of
RWD based on evidence produced by the use of these data.
Section 2: FAIR Data
• Making data findable, including provisions for metadata
All sources of data identified will be made available and metadata provided. A template to
synthesize data included in each database is in preparation and it will identify for each
dataset the following details on its content: Source of data; Type of database; Coverage
(where); Level of analysis; Coverage period; Sample size; Socio-Demographic data; Clinical
Data; Type of diagnosis classification; Type of procedure classification; Medical Device
traceable; Costs; Other individual level variables; Other hospital level variables; Other
regional level variables; Data Accessibility.
Further metadata might be added at the end of the project in line with metadata
conventions.
•
Making data openly accessible
Data availability will depend on regulation of data source producers. Where possible, we
will act as facilitators of data accessibility.
The metadata produced, as well as the list of RWD collected, will be made available
publicly.
•
Making data interoperable
Data will be deposited in a repository and measures must be taken to make it possible for
third parties to access, exploit, reproduce and disseminate —free of charge —(i) data,
including associated metadata, needed to validate the results presented in scientific
publications as soon as possible; (ii) other data, including associated metadata, as specified
We will provide information -via the repository -about tools and instruments at our disposal
necessary for validating the results
•
Increase data re-use (through clarifying licenses):
Given the aim of this WP, making usable and re-usable all collected data will address one
of its objectives, and the RWE collected will be used also for other WP of the COMED
project.
Section 3: Allocation of resources
The work to be done in making the data FAIR will be covered by the regular working budget for
producing the deliverables.
Section 4: Data security
Depending on the nature of the dataset that will be selected, traditional methods of storing data
files in a network drive or a file server would be weak solutions when it comes to complying with 21
CFR Part 11, GLP, GMP norms. LogiLab SDMS provides a controlled environment for accessing any
data that needs controlled access, audit trails and version control, and will be adopted when
needed.
4.1.2. Task 2
Section 1: Data summary
The purpose of the data generation in task 2 of WP 1 is to enable the estimation of learning curves
for medical devices by means of expert opinion, to be used when no empirical data is (yet) available.
This task contributes to COMEDs objectives, as it is often a challenge to incorporate the impact of
learning in a cost-effectiveness analysis of a medical device, so existing methods for cost and
outcome analysis can benefit from a systematic approach.
Based on findings from the literature, a questionnaire will be developed that may be used for
structured expert solicitation of information on learning curves. We might want to test this
questionnaire by asking physicians to fill in the questionnaire, and reflect on their experience. In
other words, data will be generated through a survey; no existing data will be re-used. Metadata,
i.e. a summary of the answers to the questions generated during the pilot testing of the
questionnaire, but also the resulting questionnaire itself, will be useful within other tasks of COMED,
but also in the future for other research groups who evaluate the technology of interest or who
evaluate other technologies in which the learning curve plays an important role. The expected size
of the data will be small, we will interview a maximum of 20 physicians.
Section 2: FAIR Data
• Making data findable, including provisions for metadata:
The results will be published in a scientific journal. The publication will get a unique
Digital Object Identifier, and will include the metadata (possibly in an online appendix).
•
•
•
Keywords in line with the content of the research and the terminology used in the specific
scientific field will be added to the manuscript.
Making data openly accessible
It is our intention to provide open access to the metadata generated through the survey.
Making data interoperable:
We intent to adhere to standards for formats, wherever possible, to stimulate
interoperability.
Increase data re-use (through clarifying licenses):
We will stimulate re-use of the data, as we intent to license the data under a creative
common open access agreement, with limitations for commercial re-use (i.e. re-use by
commercial entities for-profit reasons).
Section 3: Allocation of resources
The work to be done in making the data FAIR will be covered by the regular working budget for
producing the deliverables. There are no costs associated to long term preservation, but the value
of long-term preservation will diminish over time, as the case studies become less relevant.
Nevertheless, the impact of learning in general is expected to remain an important topic in costeffectiveness analyses of medical devices.
Section 4: Data security
Data are stored in selectively accessible folders on a continuously backed-up network drive of the
Erasmus University Rotterdam. Please refer to the Erasmus University Rotterdam data protection
policy, to which we adhere.
4.2. WP2: Use of surrogate outcomes for medical devices: advanced methodological
issues
4.2.1. Task2
Section 1: Data summary
The purpose of the data collection on WP2 task 2 is to illustrate the range of surrogate validation
processes and methods that could be used for the economic evaluations of medical devices. We will
seek access to anonymized patient-level data from previous randomized and non-randomized
clinical studies of selected technologies to test different surrogate validation techniques with
respect to their potential to inform an HTA report where evidence is mainly relying on surrogate
outcomes. We will collect and re-use existing data. The size of the data is currently not known but
will probably not exceed 5MB.
The data will be useful to answer the question of how good a selected surrogate endpoint is to
predict a patient-relevant outcome for the technology under investigation and to illustrate
methodological approaches to surrogacy validation. Therefore they may be useful for the scientific,
regulatory, clinical and industry community.
Section 2: FAIR Data
• Making data findable, including provisions for metadata
List of data collected and sources will be made available through a University of Exeter
repository and metadata provided.
•
Making data openly accessible
Data accessibility will depend on consent expressed by patients in the original trials. We
will ensure that datasets shared as part of the project include no patient-identifiable
information (such as names and addresses), and that all data storage complies with the
regulations governing research at University of Exeter Medical School. All data will be
received and stored in a secure database at the Clinical Trials Support Network, University
of Exeter Medical School, Exeter, United Kingdom.
•
Making data interoperable
Individual trial datasets will be combined into one overall dataset with standardised
variables, working to ensure standardisation of variables. We will provide information -via
the repository -about variables, tools and instruments to use the data.
•
Increase data re-use (through clarifying licenses)
Patient-level data from individual studies will remain the property of the
collaborators/owners who have provided them. They will retain the right to withdraw the
data from the analysis at any time. Possibility of data accessibility and re-use will depend
on consent expressed by patients in the original trials.
Section 3: Allocation of resources
The work to be done in making the data FAIR will be covered by the regular working budget for
producing the deliverables of WP2.
Section 4: Data security
All data will be received and stored in a secure database at the Clinical Trials Support Network,
University of Exeter Medical School, Exeter, United Kingdom. Data management to ensure integrity,
security and storage of this data will be performed according to the UK Clinical Research
Collaboration registered Exeter Clinical Trials Unit data management plan.
4.2.2. Task3
Section 1: Data summary
The purpose of the data generation on WP2 task 3 is to develop a methodological framework and
policy tool for the evaluation of medical devices (and other health technologies) that depends on
surrogate outcomes evidence. Data will be generated from semi-structured interviews conducted
across the EU with a purposive sample of participants belonging to different classes of stakeholders
(patients’ and carers’ organisations, healthcare professionals and their organisations, HTA
producers/assessment groups, medicines and devices manufacturers, HTA agencies’ board and
appraisal committee members and providers and commissioners of health services) and from
surveys taking the form of discrete choice experiments. Hence both qualitative and quantitative
data will be generated. The expected size of the data is currently unknown and will depend on the
sample size reached through interviews and surveys. Data will be useful to shed light on
stakeholders’ views and opinions on levers and barriers to the practical implementation of an
evidence-based policy framework for the use of surrogate outcomes evidence in policy making.
Section 2: FAIR Data
• Making data findable, including provisions for metadata
Summary of data collected and sources will be made available through a University of
Exeter repository and metadata provided.
•
Making data openly accessible
Data accessibility will depend on consent expressed by interviewees and respondents.
•
Making data interoperable
Data will be collected and stored using commonly available softwares. We will provide
information -via the repository at the University of Exeter - about tools and instruments to
use the data.
•
Increase data re-use (through clarifying licenses):
Possibility of data accessibility and re-use will depend on consent expressed by
respondents. Whenever possible, researchers will try to act as facilitators to ensure this is
made possible.
Section 3: Allocation of resources
The work to be done in making the data FAIR will be covered by the regular working budget for
producing the deliverables of WP2.
Section 4: Data security
All data will be stored in a secure database at the University of Exeter Medical School, Exeter, United
Kingdom. Data management to ensure integrity, security and storage of this data will be performed
according to the UK Clinical Research Collaboration registered Exeter Clinical Trials Unit data
management plan.
4.3. WP3: Outcome measurements and Patient Reported Outcome Measures
assessment for mHealth
4.3.1. Task 1
Section 1: Data summary
The purpose of the data collection WP3 task 1 is to provide a comprehensive overview of existing
methods to measure outcomes and PROMs for mHealth technologies.
For this specific task, we will thus collect and then use existing data.
The collection of such data will be instrumental in developing a theoretical and methodological
framework to help push the boundaries of outcome analysis for such technologies, in accordance
with the overarching goal of the COMED project.
The sources that will be explored to collect evidence on will be prevalently peer-reviewed
manuscripts and reviews. Further sources that will be analysed are experimental studies protocols
accessible from online databases.
The data will be useful for other project partners and for future research groups that will work on
this research topic. The evidence produced will be advantageous for policymakers and practitioners
as well, by providing them with state-of-the-art insight on measures and applications for assessing
patient reported outcomes measures within mHealth settings.
Different sources of data will be collected for other tasks of this WP: the related DMP will be detailed
accordingly in future updates of this document.
Section 2: FAIR Data
• Making data findable, including provisions for metadata
All sources of data identified will be made available and metadata provided. Specific
keywords will be added to make the dataset findable for different researchers.
A template to synthesize data included in each database will be identified and will include,
for each dataset, some of the following details on its contents: Type of database; Coverage
(where); Coverage period; Sample size; Type of PROMs administered; Frequency of
administration; Study design; Primary endpoint; Secondary endpoints; Socio-Demographic
data; Other individual level variables; Data Accessibility.
Further metadata might be added at the end of the project in line with metadata
conventions.
•
Making data openly accessible
The metadata produced will be made publicly available and will include all sources
explored, as long as data availability is guaranteed for each of. Where possible, we will act
as facilitators of data accessibility.
•
Making data interoperable
Metadata will be stored in a trusted and widely accessible data repository. All possible
measures will be undertaken to make it possible for third parties to access, exploit,
reproduce and disseminate —free of charge – all data types present in the aforementioned
datasets.
We will facilitate the interoperability of the data collected by adhering to existing
standards.
•
Increase data re-use (through clarifying licenses):
The collected data will be available for re-use by both third parties and COMED partners.
Making all collected data re-usable is part of the WP objectives and will be instrumental in
shaping the following objectives of the projected work.
Section 3: Allocation of resources
The work to be done in making the data FAIR will be covered by the regular working budget for
producing the deliverables. No specific costs are to be foreseen in the FAIR making process of our
data.
Section 4: Data security
Depending on the nature of the dataset that will be selected, traditional methods of storing data
files in a network drive or a file server would be weak solutions when it comes to complying with
21 CFR Part 11, GLP, GMP norms. Data will be kept on secure servers and environments.
4.4. WP4 & WP5: Variation in the use of medical devices
Section 1: Data summary
The task on WP 4/ 5 is to develop and use a model explaining geographical variation in the use of
different medical technologies. The analysis will focus on showing and explaining warranted and
unwarranted geographic variation within and between the different participating countries.
The purpose of the data collection, specifically in WP 5 Task 2 is to develop a data library suitable
for the investigation of the different use of medical devices. Therefore, we will develop a database,
which will give information on the prevalence of disease and the use of procedures, which treat the
diseases. Additional patient, provider or general explanatory variables will be used to analyse
differences in the usage of the medical devices.
We will collect and then use existing data from the participating countries.
Possible sources are administrative databases on diagnoses and procedures in inpatient and
outpatient care either countrywide or related to a sickness fund. Furthermore, databases on
structural and socio-economic variables are of interest.
Section 2: FAIR Data
• Making data findable, including provisions for metadata
All sources of data identified will be made available and metadata will be provided.
Furthermore, a list of used variables and the corresponding dataset in each country will be
published. Publicly available data or data without privacy restriction will be published.
• Making data openly accessible
Data availability will depend on regulation of data owners. Since most of the data is not
publicly available, but restricted due to data protection we aim on providing aggregated
information (e.g. on geographical levels such as NUTS level (Nomenclature des unités
territoriales statistiques)
The metadata produced, as well as the list of variables collected, will be made available
publicly.
• Making data interoperable
The data collected are usually from non-public sources such as administrative data from
sickness funds or patient data collected for research purposes or the development of
reimbursement systems. The publication and detailed description of variables used for the
analysis and corresponding years within each dataset will ensure the interoperability of the
data.
• Increase data re-use (through clarifying licenses):
Due to the expected strict licences and privacy restrictions a re-use of the dataset will
only be possible to a limited extent respecting the privacy regulations of data owners in
each countries.
Section 3: Allocation of resources
The work to be done in making the data FAIR will be covered by the regular working budget for
producing the deliverables.
Section 4: Data security
Data will be stored, used and analysed according to the country specific requirements for each
dataset. All patient related data will be anonymized before our analysis.
4.5. WP6: Early dialogue and early assessment of medical devices
Data of WP6 are collected and generated with the aim to develop methodological and policy
guidelines that improve the early dialogue between manufacturers, regulators/notified bodies and
HTABs in order to overcome barriers to HTA. WP6 contributes to COMEDs objectives by streamlining
the HTA process by facilitating the alignment of evidentiary requirements.
4.5.1. Task 2
Section 1: Data summary
The Survey consists in questionnaires where qualitative primary data will be generated. The
respondents are selected from EU HTA agencies, notified bodies and manufacturers of medical
devices. At this stage data size cannot be estimated. Due to the qualitative character of the data,
the dataset most likely will not be large. The raw data will be interesting for researchers in the field
of early dialogue or barriers of HTA. Raw data will not be of great value for the three parties, if not
presented in guidelines, a report or article.
Section 2: FAIR Data
• Making data findable, including provisions for metadata:
o The processed data in form of articles will be findable via data base search engines
(PubMed, Google Scholar) and will connected to a publishers DOI.
o Raw data will not be made findable.
• Making data openly accessible
o Individual level data will not be published publicly. Data from the EMA parallel regulatoryHTA scientific advice is strictly confidential. Anonymized raw data might be shared with
researchers requesting it.
o Published as scientific articles in peer reviewed journals.
•
Making data interoperable:
Not applicable. The research will not produce data suitable for a database.
•
Increase data re-use (through clarifying licenses):
Most likely data will be reused by partners or researchers reading our article and
requesting the raw data.
4.5.2. Task 3
Section 1: Data summary
Case studies will generate qualitative primary data and secondary data, including interview,
documents, registries, reports. Existing data will be re-used as well as new data will be generated.
Data will be originated from various sources: Clinicaltrials.gov, EPARs, HTAB Dossier assessment
reports, Manufacturers, regulators and HTA agencies that participated in an early dialogue. As for
the survey, it is not possible to estimate the data size. Due to the qualitative character of the data,
the dataset is not expected to be large.
The raw data will be interesting for researchers in the field of early dialogue or barriers of HTA. Raw
data will not be of great value for the three parties, if not presented in guidelines, a report or article.
Section 2: FAIR Data
• Making data findable, including provisions for metadata:
o The processed data in form of articles will be findable via data base search engines
(PubMed, Google Scholar) and will connected to a publishers DOI.
o Raw data will not be made findable.
• Making data openly accessible
o Individual level data will not be published publicly. Data from the EMA parallel regulatoryHTA scientific advice is strictly confidential. Anonymized raw data might be shared with
researchers requesting it.
o Published as scientific articles in peer reviewed journals.
• Making data interoperable:
Not applicable. The research will not produce data suitable for a database.
• Increase data re-use (through clarifying licenses):
Most likely data will be reused by partners or researchers reading our article and
requesting the raw data.
Sections 3 and 4 apply to all Tasks.
Section 3: Allocation of resources
No extra costs, University of Bern has an Open Data Repository BORIS (Bern Open Repository and
Information System) where articles (if possible) and other data could be made available.
The work to be done in making the data FAIR will be covered by the regular working budget for
producing the deliverables.
Section 4: Data security
To ensure confidentiality and to guarantee internationally-accepted replicability standards, data
will be analysed and stored with code numbers, never any information that could be used to
identify participants. Data will be kept on secure servers and password-protected computers. The
data will be stored after the termination of the current research for a period no shorter than 6
years, and at no time will any identifying information about the participants be stored along with
the data.
All responses will be held in confidence. Only the researchers involved in this study and those
responsible for research oversight will have access to the information. Anonymized records may
be shared with other professionals or authorities from University of Bern, who may be evaluating
or replicating the study, provided the data owner grants them access to the data.
4.6. WP7: Coverage with evidence development for medical devices
4.6.1. Task 3
Section 1: Data summary
The overall purpose of WP7 is to develop a taxonomy of coverage with evidence (CED) schemes
currently applied to medical devices in Europe, and to subsequently propose a policy guide for those
wishing to design and implement CED schemes in the future.
In order to ensure maximum impact, the policy guide will be validated by discussing it with key
policy-makers in the national bodies currently conducting CED schemes (task 3).
Therefore within task 3 we will generate new survey data through structured interviews conducted
by all the partners participating in the work package.
The data may be useful for other project partners (e.g. for WP8 on early HTA) and in the future for
other research groups; and indirectly for policymakers, that will be able to initiate CED schemes for
Medical Devices based on evidence produced by the use of these data.
Section 2: FAIR Data
• Making data findable, including provisions for metadata
We plan to generate a unique dataset containing the responses of the policy makers
participating to the interview. This data will be made available and metadata provided.
Further metadata might be added at the end of the project in line with metadata
conventions.
•
Making data openly accessible
Data from the surveys will be anonymized to guarantee the privacy of the participants, and
then made available publicly. Depending on the type of information eventually generated,
we will make available either the raw transcripts of the interviews, or a summary of the
individual responses to each question in the survey.
The data will be stored in a trusted repository as indicated in the present Data Management
Plan (section 3.2).
•
Making data interoperable
Data will be stored in a conventional file format. Particularly we will use spreadsheets or
text documents that are compatible with open source software applications.
•
Increase data re-use (through clarifying licenses):
We expect to license the data under an Open Access license. The duration of data
availability will be defined later in the current research project, and will consider a
reasonable amount of time after which data will be no longer considered up to date or
relevant for other potential users.
All other general rules to make the data compliant with the FAIR principles, which are described in
the present DMP (section 3), also apply to this WP.
Section 3: Allocation of resources
The work to be done in making the data FAIR will be covered by the regular working budget for
producing the deliverables.
Section 4: Data security
Data will be kept on secure servers and password-protected computers or flash drives. All physical
data will be stored in locked filing cabinets that only members of the research team have access
to. Identifying information (names, contact details, affiliation) will be kept separately from the
scripts which will be anonymous and no identifiable details will be given in reports and
publications, unless explicitly stated by the participants.
4.7. WP8: Transferability of medical device HTA/EE and of evidence on uncertainty
factors across EU Member States
4.7.1. Task 1
Section 1: Data summary
The purpose of data collection in WP8 task 1 is to assess the transferability of outcome evaluation
using the real world evidence and learning curves for medical devices across EU countries. The
transferability of data will be evaluated based on 1) the feasibility of collecting real-world
effectiveness and safety data in countries with limited resources for HTA, also considering 2) the
heterogeneity of health systems and 3) differences in real world effect size as a barrier to clinical
outcome data transferability across countries. The possible sources of data for evaluating
transferability are surveys, structured and semi-structured interviews, databases, registries and
patient chart reviews. Existing data will be used for evaluation. Task 1 is strongly linked to WP1,
therefore the methodology of data collection is dependent on the outcomes of WP1. The data will
be useful for other research groups; and for policymakers, who will be able to apply methods to
evaluate the transferability of evidence to jurisdictions where local studies are not feasible.
Section 2: FAIR Data
• Making data findable, including provisions for metadata:
o Summary of collected and processed data will be findable via database search engines
in form of reports and articles.
o Raw data will not be made findable.
• Making data openly accessible
o Individual level data will not be published publicly.
o A summary of the report on transferability of real world evidence will be published as
scientific article in peer reviewed journal.
•
Making data interoperable:
Not applicable. The research will not produce data suitable for a database.
•
Increase data re-use (through clarifying licenses):
o Possibility of data accessibility and re-use will depend on consent expressed by
respondents. Whenever possible, researchers will try to act as facilitators to ensure this
is made possible.
Section 3: Allocation of resources
The work to be done in making the data FAIR will be covered by the regular working budget for
producing the deliverables.
Section 4: Data security
The information collected in WP8 will be kept confidential and stored in a secure database at the
Syreon Research Institute. All individual-level information will be made anonymous. Only the
researchers involved in this study and those responsible for research oversight will have access to
the information collected.
4.7.2. Task 2
Section 1: Data summary
The purpose of data collection in WP8 Task 2 is to assess the requirements for the acceptability and
feasibility of performance based risk sharing agreements (such as coverage with evidence
development) based on foreign data in Member States in which local studies are not feasible. Focus
group discussions will be designed and conducted across invited representatives of reimbursement
decision makers from the selected Member States. A summary of the focus group report will be
channelled into the guideline development on data collection in WP8, and will be discussed with a
group of medical devices reimbursement decision makers and manufacturers from countries with
sufficient economic and geographical diversity. The focus group report will be useful for other
research groups; and for policymakers and payers in the field of reimbursement of medical devices.
Section 2: FAIR Data
• Making data findable, including provisions for metadata:
o The focus group report will be findable via database search engines in form of report
and article.
o Raw data will not be made findable.
• Making data openly accessible
o The dataset collected in the focus group (e.g. transcript) will have restricted access.
o A summary of the focus group report will be published as scientific article in peer
reviewed journal. The data included in the publication will therefore be automatically
open access in order to make data accessible for verification and re-use.
•
Making data interoperable:
Not applicable. The research will not produce data suitable for a database.
•
Increase data re-use (through clarifying licenses):
o Possibility of data accessibility and re-use will depend on consent expressed by
respondents. Whenever possible, researchers will try to act as facilitators to ensure this
is made possible.
Section 3: Allocation of resources
The work to be done in making the data FAIR will be covered by the regular working budget for
producing the deliverables.
Section 4: Data security
Participation in the focus group will be purely voluntary and all participants will provide informed
consent to take part in the study. In the analysis, data that may reveal subjects’ identities will be
anonymised, in addition, pharmaceutical companies or healthcare funds will never be mentioned in
an identifiable manner. The information collected in WP8 will be kept confidential and stored in a
secure database at the Syreon Research Institute. Only the researchers involved in this study and
those responsible for research oversight will have access to the information collected.
4.7.3. Task 3
Section 1: Data summary
The purpose of data collection in WP8 Task 3 is to test the relevance of policy tools developed in
the different work packages of COMED (WP2, WP3, WP6 and WP7) and their transferability to lower
income countries. Representatives of policy makers (involved in reimbursement decisions) and
manufacturers will be invited into a satellite mini-conference from interested EU Member States,
with an emphasis of sufficient representation of lower income CEE and Southern EU countries.
Research plans, results and conclusions will be presented and discussed with the invited
stakeholders, and the collected feedback will be summarized in a conference report, to be
channelled into guideline development in WP9.
The conference report will be useful for other research groups; and for policymakers and payers in
the field of health technology assessment and economic evaluation of medical devices.
Section 2: FAIR Data
• Making data findable, including provisions for metadata:
o The conference report will be findable via database search engines in form of report
and article.
o Raw data will not be made findable.
• Making data openly accessible
o Individual level data will not be published publicly.
o A summary of the conference report will be published as scientific article in peer
reviewed journal.
•
Making data interoperable:
Not applicable. The research will not produce data suitable for a database.
•
Increase data re-use (through clarifying licenses):
o Possibility of data accessibility and re-use will depend on consent expressed by
respondents. Whenever possible, researchers will try to act as facilitators to ensure this
is made possible.
Section 3: Allocation of resources
The work to be done in making the data FAIR will be covered by the regular working budget for
producing the deliverables.
Section 4: Data security
The information collected in WP8 will be kept confidential and stored in a secure database at the
Syreon Research Institute. All individual-level information will be made anonymous. Only the
researchers involved in this study and those responsible for research oversight will have access to
the information collected.