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      Obtaining and managing data sets for individual participant data meta-analysis: scoping review and practical guide

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          Abstract

          Background

          Shifts in data sharing policy have increased researchers’ access to individual participant data (IPD) from clinical studies. Simultaneously the number of IPD meta-analyses (IPDMAs) is increasing. However, rates of data retrieval have not improved. Our goal was to describe the challenges of retrieving IPD for an IPDMA and provide practical guidance on obtaining and managing datasets based on a review of the literature and practical examples and observations.

          Methods

          We systematically searched MEDLINE, Embase, and the Cochrane Library, until January 2019, to identify publications focused on strategies to obtain IPD. In addition, we searched pharmaceutical websites and contacted industry organizations for supplemental information pertaining to recent advances in industry policy and practice. Finally, we documented setbacks and solutions encountered while completing a comprehensive IPDMA and drew on previous experiences related to seeking and using IPD.

          Results

          Our scoping review identified 16 articles directly relevant for the conduct of IPDMAs. We present short descriptions of these articles alongside overviews of IPD sharing policies and procedures of pharmaceutical companies which display certification of Principles for Responsible Clinical Trial Data Sharing via Pharmaceutical Research and Manufacturers of America or European Federation of Pharmaceutical Industries and Associations websites. Advances in data sharing policy and practice affected the way in which data is requested, obtained, stored and analyzed.

          For our IPDMA it took 6.5 years to collect and analyze relevant IPD and navigate additional administrative barriers. Delays in obtaining data were largely due to challenges in communication with study sponsors, frequent changes in data sharing policies of study sponsors, and the requirement for a diverse skillset related to research, administrative, statistical and legal issues.

          Conclusions

          Knowledge of current data sharing practices and platforms as well as anticipation of necessary tasks and potential obstacles may reduce time and resources required for obtaining and managing data for an IPDMA. Sufficient project funding and timeline flexibility are pre-requisites for successful collection and analysis of IPD. IPDMA researchers must acknowledge the additional and unexpected responsibility they are placing on corresponding study authors or data sharing administrators and should offer assistance in readying data for sharing.

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          Most cited references92

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          A systematic review of barriers to data sharing in public health

          Background In the current information age, the use of data has become essential for decision making in public health at the local, national, and global level. Despite a global commitment to the use and sharing of public health data, this can be challenging in reality. No systematic framework or global operational guidelines have been created for data sharing in public health. Barriers at different levels have limited data sharing but have only been anecdotally discussed or in the context of specific case studies. Incomplete systematic evidence on the scope and variety of these barriers has limited opportunities to maximize the value and use of public health data for science and policy. Methods We conducted a systematic literature review of potential barriers to public health data sharing. Documents that described barriers to sharing of routinely collected public health data were eligible for inclusion and reviewed independently by a team of experts. We grouped identified barriers in a taxonomy for a focused international dialogue on solutions. Results Twenty potential barriers were identified and classified in six categories: technical, motivational, economic, political, legal and ethical. The first three categories are deeply rooted in well-known challenges of health information systems for which structural solutions have yet to be found; the last three have solutions that lie in an international dialogue aimed at generating consensus on policies and instruments for data sharing. Conclusions The simultaneous effect of multiple interacting barriers ranging from technical to intangible issues has greatly complicated advances in public health data sharing. A systematic framework of barriers to data sharing in public health will be essential to accelerate the use of valuable information for the global good. Electronic supplementary material The online version of this article (doi:10.1186/1471-2458-14-1144) contains supplementary material, which is available to authorized users.
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            Get real in individual participant data (IPD) meta‐analysis: a review of the methodology

            Individual participant data (IPD) meta‐analysis is an increasingly used approach for synthesizing and investigating treatment effect estimates. Over the past few years, numerous methods for conducting an IPD meta‐analysis (IPD‐MA) have been proposed, often making different assumptions and modeling choices while addressing a similar research question. We conducted a literature review to provide an overview of methods for performing an IPD‐MA using evidence from clinical trials or non‐randomized studies when investigating treatment efficacy. With this review, we aim to assist researchers in choosing the appropriate methods and provide recommendations on their implementation when planning and conducting an IPD‐MA. © 2015 The Authors. Research Synthesis Methods published by John Wiley & Sons, Ltd.
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              Assessment of publication bias, selection bias, and unavailable data in meta-analyses using individual participant data: a database survey.

              To examine the potential for publication bias, data availability bias, and reviewer selection bias in recently published meta-analyses that use individual participant data and to investigate whether authors of such meta-analyses seemed aware of these issues. In a database of 383 meta-analyses of individual participant data that were published between 1991 and March 2009, we surveyed the 31 most recent meta-analyses of randomised trials that examined whether an intervention was effective. Identification of relevant articles and data extraction was undertaken by one author and checked by another. Only nine (29%) of the 31 meta-analyses included individual participant data from "grey literature" (such as unpublished studies) in their primary meta-analysis, and the potential for publication bias was discussed or investigated in just 10 (32%). Sixteen (52%) of the 31 meta-analyses did not obtain all the individual participant data requested, yet five of these (31%) did not mention this as a potential limitation, and only six (38%) examined how trials without individual participant data might affect the conclusions. In nine (29%) of the meta-analyses reviewer selection bias was a potential issue, as the identification of relevant trials was either not stated or based on a more selective, non-systematic approach. Investigation of four meta-analyses containing data from ≥10 trials revealed one with an asymmetric funnel plot consistent with publication bias, and the inclusion of studies without individual participant data revealed additional heterogeneity between trials. Publication, availability, and selection biases are a potential concern for meta-analyses of individual participant data, but many reviewers neglect to examine or discuss them. These issues warn against uncritically viewing any meta-analysis that uses individual participant data as the most reliable. Reviewers should seek individual participant data from all studies identified by a systematic review; include, where possible, aggregate data from any studies lacking individual participant data to consider their potential impact; and investigate funnel plot asymmetry in line with recent guidelines.
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                Author and article information

                Contributors
                ventrem@mcmaster.ca
                schuneh@mcmaster.ca
                fergus.macbeth@btinternet.com
                m.clarke@qub.ac.uk
                thabanl@mcmaster.ca
                G.O.Griffiths@soton.ac.uk
                Simon.Noble@wales.nhs.uk
                davidg99@uw.edu
                marcum2@mcmaster.ca
                iorioa@mcmaster.ca
                qzhou@mcmaster.ca
                crowthrm@mcmaster.ca
                ea32@aub.edu.lb
                glyman@fredhutch.org
                viktoria.gloy@usb.ch
                mdinisio@unich.it
                matthias.briel@usb.ch
                Journal
                BMC Med Res Methodol
                BMC Med Res Methodol
                BMC Medical Research Methodology
                BioMed Central (London )
                1471-2288
                12 May 2020
                12 May 2020
                2020
                : 20
                : 113
                Affiliations
                [1 ]GRID grid.25073.33, ISNI 0000 0004 1936 8227, Department of Health Research Methods, Evidence, and Impact, , McMaster University, ; Hamilton, Ontario Canada
                [2 ]GRID grid.5600.3, ISNI 0000 0001 0807 5670, Centre for Trials Research, School of Medicine, , Cardiff University, ; Cardiff, Wales, UK
                [3 ]GRID grid.4777.3, ISNI 0000 0004 0374 7521, Northern Ireland Hub for Trials Methodology Research and Cochrane Individual Participant Data Meta-analysis Methods Group, , Queen’s University Belfast, ; Belfast, UK
                [4 ]GRID grid.5491.9, ISNI 0000 0004 1936 9297, Wales Cancer Trials Unit, School of Medicine, , Cardiff University, Wales, UK; Faculty of Medicine, University of Southampton, ; Southampton General Hospital, Southampton, UK
                [5 ]GRID grid.5600.3, ISNI 0000 0001 0807 5670, Marie Curie Palliative Care Research Centre, , Cardiff University, ; Cardiff, Wales, UK
                [6 ]GRID grid.34477.33, ISNI 0000000122986657, Department of Medicine, , University of Washington School of Medicine, ; Seattle, WA USA
                [7 ]GRID grid.25073.33, ISNI 0000 0004 1936 8227, Department of Medicine, , McMaster University, ; Hamilton, Ontario Canada
                [8 ]GRID grid.22903.3a, ISNI 0000 0004 1936 9801, Department of Internal Medicine, , American University of Beirut, ; Beirut, Lebanon
                [9 ]GRID grid.34477.33, ISNI 0000000122986657, Department of Medicine, , University of Washington School of Medicine, ; Seattle, Washington, USA
                [10 ]GRID grid.270240.3, ISNI 0000 0001 2180 1622, Hutchinson Institute for Cancer Outcomes Research, Fred Hutchinson Cancer Research Center, ; Seattle, Washington USA
                [11 ]GRID grid.6612.3, ISNI 0000 0004 1937 0642, Basel Institute for Clinical Epidemiology and Biostatistics, Department of Clinical Research, , University of Basel and University Hospital Basel, ; Basel, Switzerland
                [12 ]GRID grid.412451.7, ISNI 0000 0001 2181 4941, Department of Medicine and Ageing Sciences, , University G. D’Annunzio, ; Chieti-Pescara, Italy
                Article
                964
                10.1186/s12874-020-00964-6
                7218569
                32398016
                92b88f32-c613-4285-91e1-5bfda85838d1
                © The Author(s) 2020

                Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                History
                : 27 September 2019
                : 30 March 2020
                Funding
                Funded by: Canadian Institutes of Health Research
                Award ID: Knowledge Synthesis Grant, KRS 126594
                Categories
                Research Article
                Custom metadata
                © The Author(s) 2020

                Medicine
                individual participant data meta-analysis,data collection,data sharing,systematic review,practical guide

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