**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 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  2  ,
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.

##  **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. 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 

  

  2. to investigate health system performance through analysis of variation in costs and outcomes across different geographical areas 

  

  3. 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.

**Table 1.** COMED Data  
  
<table>  
<tr>  
<th>

**WP**

</th>  
<th>

**Task**

</th>  
<th>

**Dataset name**

</th>  
<th>

**Data collected/generated**

</th>  
<th>

**Timelines**

</th>  
<th>

**PI**

</th> </tr>  
<tr>  
<td>

1

</td>  
<td>

1

</td>  
<td>

RWD Mapping

</td>  
<td>

Collected

</td>  
<td>

M1-M12

</td>  
<td>

UB

</td> </tr>  
<tr>  
<td>

2

</td>  
<td>

Expert solicitation

Learning Curve

</td>  
<td>

Generated

</td>  
<td>

M6-M12

</td>  
<td>

EUR

</td> </tr>  
<tr>  
<td>

2

</td>  
<td>

2

</td>  
<td>

Surrogate Outcome

Mapping

</td>  
<td>

Collected

</td>  
<td>

M6-30

</td>  
<td>

UEMS

</td> </tr>  
<tr>  
<td>

</td>  
<td>

3

</td>  
<td>

Semi-structured interviews on Surrogate

Outcomes

</td>  
<td>

Generated

</td>  
<td>

M18-30

</td>  
<td>

UEMS

</td> </tr>  
<tr>  
<td>

3

</td>  
<td>

1

</td>  
<td>

mHealth Mapping

</td>  
<td>

Collected

</td>  
<td>

M6-12

</td>  
<td>

UB

</td> </tr>  
<tr>  
<td>

4&5

</td>  
<td>

</td>  
<td>

Data library

</td>  
<td>

Collected

</td>  
<td>

M6-M18

</td>  
<td>

HCHE

</td> </tr>  
<tr>  
<td>

6

</td>  
<td>

2

</td>  
<td>

Expert survey Early

Dialogue

</td>  
<td>

Generated

</td>  
<td>

M6-M18

</td>  
<td>

UBERN

</td> </tr>  
<tr>  
<td>

3

</td>  
<td>

Case Study Early Dialogue

</td>  
<td>

Collected/Generated

</td>  
<td>

M6-M24

</td>  
<td>

UBERN

</td> </tr>  
<tr>  
<td>

7

</td>  
<td>

1

</td>  
<td>

Interviews

</td>  
<td>

Generated

</td>  
<td>

M24-M30

</td>  
<td>

UB

</td> </tr>  
<tr>  
<td>

8

</td>  
<td>

1

</td>  
<td>

RWE on Transferability of

MD HTA/EE

</td>  
<td>

Collected

</td>  
<td>

M6-M12

</td>  
<td>

SYREON

</td> </tr>  
<tr>  
<td>

2

</td>  
<td>

Focus group

</td>  
<td>

Generated

</td>  
<td>

M18-24

</td>  
<td>

SYREON

</td> </tr>  
<tr>  
<td>

3

</td>  
<td>

Stakeholders miniconference

</td>  
<td>

Generated

</td>  
<td>

M18-M33

</td>  
<td>

SYREON

</td> </tr> </table>

###  _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  3  , to inform individual research subjects about all aspects of
the research in which they are being asked to participate, 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
4  . 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 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: _

    * 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. 

    * 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: _

    * 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. 

  

    * 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. 

    * 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: _

    * 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. 

    * 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): _

    * 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: _

    * 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): _

    * 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: _

    * 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. 

    * 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): _

    * 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.