Sharing and reuse of individual participant data from clinical trials: principles and recommendations

A practical guide to data management and sharing for biomedical laboratory researchers

Effective data management and sharing have become increasingly crucial in biomedical research; however, many laboratory researchers lack the necessary tools and knowledge to address this challenge. This article provides an introductory guide into research data management (RDM), and the importance of FAIR (Findable, Accessible, Interoperable, and Reusable) data-sharing principles for laboratory researchers produced by practicing scientists. We explore the advantages of implementing organized data management strategies and introduce key concepts such as data standards, data documentation, and the distinction between machine and human-readable data formats. Furthermore, we offer practical guidance for creating a data management plan and establishing efficient data workflows within the laboratory setting, suitable for labs of all sizes. This includes an examination of requirements analysis, the development of a data dictionary for routine data elements, the implementation of unique subject identifiers, and the formulation of standard operating procedures (SOPs) for seamless data flow. To aid researchers in implementing these practices, we present a simple organizational system as an illustrative example, which can be tailored to suit individual needs and research requirements. By presenting a user-friendly approach, this guide serves as an introduction to the field of RDM and offers practical tips to help researchers effortlessly meet the common data management and sharing mandates rapidly becoming prevalent in biomedical research.

[Nationally standardized broad consent in practice: initial experiences, current developments, and critical assessment]

BACKGROUND

The digitalization in the healthcare sector promises a secondary use of patient data in the sense of a learning healthcare system. For this, the Medical Informatics Initiative's (MII) Consent Working Group has created an ethical and legal basis with standardized consent documents. This paper describes the systematically monitored introduction of these documents at the MII sites.

METHODS

The monitoring of the introduction included regular online surveys, an in-depth analysis of the introduction processes at selected sites, and an assessment of the documents in use. In addition, inquiries and feedback from a large number of stakeholders were evaluated.

RESULTS

The online surveys showed that 27 of the 32 sites have gradually introduced the consent documents productively, with a current total of 173,289 consents. The analysis of the implementation procedures revealed heterogeneous organizational conditions at the sites. The requirements of various stakeholders were met by developing and providing supplementary versions of the consent documents and additional information materials.

DISCUSSION

The introduction of the MII consent documents at the university hospitals creates a uniform legal basis for the secondary use of patient data. However, the comprehensive implementation within the sites remains challenging. Therefore, minimum requirements for patient information and supplementary recommendations for best practice must be developed. The further development of the national legal framework for research will not render the participation and transparency mechanisms developed here obsolete.

Serum 25-hydroxyvitamin D threshold and risk of rickets in young children: a systematic review and individual participant data meta-analysis to inform the development of dietary requirements for vitamin D

PURPOSE

The objective of this systematic review was to determine a minimum serum 25-hydroxyvitamin D (25OHD) threshold based on the risk of having rickets in young children. This work was commissioned by the WHO and FAO within the framework of the update of the vitamin D requirements for children 0-3 years old.

METHODS

A systematic search of Embase was conducted to identify studies involving children below  4 years of age with serum 25OHD levels and radiologically confirmed rickets, without any restriction related to the geographical location or language. Study-level and individual participant data (IPD)-level random effects multi-level meta-analyses were conducted. The odds, sensitivity and specificity for rickets at different serum 25OHD thresholds were calculated for all children as well as for children with adequate calcium intakes only.

RESULTS

A total of 120 studies with 5412 participants were included. At the study-level, children with rickets had a mean serum 25OHD of 23 nmol/L (95% CI 19-27). At the IPD level, children with rickets had a median and mean serum 25OHD of 23 and 29 nmol/L, respectively. More than half (55%) of the children with rickets had serum 25OHD below 25 nmol/L, 62% below 30 nmol/L, and 79% below 40 nmol/L. Analysis of odds, sensitivities and specificities for nutritional rickets at different serum 25OHD thresholds suggested a minimal risk threshold of around 28 nmol/L for children with adequate calcium intakes and 40 nmol/L for children with low calcium intakes.

CONCLUSION

This systematic review and IPD meta-analysis suggests that from a public health perspective and to inform the development of dietary requirements for vitamin D, a minimum serum 25OHD threshold of around 28 nmol/L and above would represent a low risk of nutritional rickets for the majority of children with an adequate calcium intake.

An assessment of the informative value of data sharing statements in clinical trial registries

BACKGROUND

The provision of data sharing statements (DSS) for clinical trials has been made mandatory by different stakeholders. DSS are a device to clarify whether there is intention to share individual participant data (IPD). What is missing is a detailed assessment of whether DSS are providing clear and understandable information about the conditions for data sharing of IPD for secondary use.

METHODS

A random sample of 200 COVID-19 clinical trials with explicit DSS was drawn from the ECRIN clinical research metadata repository. The DSS were assessed and classified, by two experienced experts and one assessor with less experience in data sharing (DS), into different categories (unclear, no sharing, no plans, yes but vague, yes on request, yes with specified storage location, yes but with complex conditions).

RESULTS

Between the two experts the agreement was moderate to substantial (kappa=0.62, 95% CI [0.55, 0.70]). Agreement considerably decreased when these experts were compared with a third person who was less experienced and trained in data sharing ("assessor") (kappa=0.33, 95% CI [0.25, 0.41]; 0.35, 95% CI [0.27, 0.43]). Between the two experts and under supervision of an independent moderator, a consensus was achieved for those cases, where both experts had disagreed, and the result was used as "gold standard" for further analysis. At least some degree of willingness of DS (data sharing) was expressed in 63.5% (127/200) cases. Of these cases, around one quarter (31/127) were vague statements of support for data sharing but without useful detail. In around half of the cases (60/127) it was stated that IPD could be obtained by request. Only in in slightly more than 10% of the cases (15/127) it was stated that the IPD would be transferred to a specific data repository. In the remaining cases (21/127), a more complex regime was described or referenced, which could not be allocated to one of the three previous groups. As a result of the consensus meetings, the classification system was updated.

CONCLUSION

The study showed that the current DSS that imply possible data sharing are often not easy to interpret, even by relatively experienced staff. Machine based interpretation, which would be necessary for any practical application, is currently not possible. Machine learning and / or natural language processing techniques might improve machine actionability, but would represent a very substantial investment of research effort. The cheaper and easier option would be for data providers, data requestors, funders and platforms to adopt a clearer, more structured and more standardised approach to specifying, providing and collecting DSS.

TRIAL REGISTRATION

The protocol for the study was pre-registered on ZENODO ( https://zenodo.org/record/7064624#.Y4DIAHbMJD8 ).

Data resource profile: the virtual international care homes trials archive (VICHTA)

Introduction

Randomised controlled trials (RCTs) conducted in care home settings address a range of health conditions impacting older people, but often include a common core of data about residents and the care home environment. These data can be used to inform service provision, but accessing these data can be challenging.

Methods

The Virtual International Care Home Trials Archive (VICHTA) collates care home RCTs conducted since 2010, with >100 participants, across multiple conditions, with documented eligibility criteria, initially identified from a scoping review. A Steering Committee comprising contributing trialists oversees proposed uses of fully anonymised data. We characterised available demography and outcomes to inform potential analyses. Data are accessible via application to the Virtual Trials Archives, through a secure online analysis platform. Trial recruitment is ongoing and future expansion will include international studies.

Results

The first phase of VICHTA includes data from six UK RCTs, with individual participant data (IPD) on 5,674 residents across 308 care homes. IPD include age, sex, dementia status, length of stay, quality of life, clinical outcome measures, medications, resource use, and care home characteristics, such as funding, case mix, and occupancy. Follow-up ranges between four and sixteen months.

Conclusions

VICHTA collates and makes accessible data on a complex and under-represented research population for novel analyses, and to inform design of future studies. Planned expansion to international care home RCTs will facilitate a wider range of research questions. Interested collaborators can submit trial data or request data at http://www.virtualtrialsarchives.org.

Towards Fair Patient-Trial Matching via Patient-Criterion Level Fairness Constraint

Clinical trials are indispensable in developing new treatments, but they face obstacles in patient recruitment and retention, hindering the enrollment of necessary participants. To tackle these challenges, deep learning frameworks have been created to match patients to trials. These frameworks calculate the similarity between patients and clinical trial eligibility criteria, considering the discrepancy between inclusion and exclusion criteria. Recent studies have shown that these frameworks outperform earlier approaches. However, deep learning models may raise fairness issues in patient-trial matching when certain sensitive groups of individuals are underrepresented in clinical trials, leading to incomplete or inaccurate data and potential harm. To tackle the issue of fairness, this work proposes a fair patient-trial matching framework by generating a patient-criterion level fairness constraint. The proposed framework considers the inconsistency between the embedding of inclusion and exclusion criteria among patients of different sensitive groups. The experimental results on real-world patient-trial and patient-criterion matching tasks demonstrate that the proposed framework can successfully alleviate the predictions that tend to be biased.

Sharing Medical Big Data While Preserving Patient Confidentiality in Innovative Medicines Initiative: A Summary and Case Report from BigData@Heart

Sharing individual patient data (IPD) is a simple concept but complex to achieve due to data privacy and data security concerns, underdeveloped guidelines, and legal barriers. Sharing IPD is additionally difficult in big data-driven collaborations such as Bigdata@Heart in the Innovative Medicines Initiative, due to competing interests between diverse consortium members. One project within BigData@Heart, case study 1, needed to pool data from seven heterogeneous data sets: five randomized controlled trials from three different industry partners, and two disease registries. Sharing IPD was not considered feasible due to legal requirements and the sensitive medical nature of these data. In addition, harmonizing the data sets for a federated data analysis was difficult due to capacity constraints and the heterogeneity of the data sets. An alternative option was to share summary statistics through contingency tables. Here it is demonstrated that this method along with anonymization methods to ensure patient anonymity had minimal loss of information. Although sharing IPD should continue to be encouraged and strived for, our approach achieved a good balance between data transparency while protecting patient privacy. It also allowed a successful collaboration between industry and academia.

Heterogeneity and Utility of Pharmaceutical Company Sharing of Individual-Participant Data Packages

Importance

The pharmaceutical industry has made substantial investments in developing processes for sharing individual-participant data (IPD) from clinical trials. However, the utility and completeness of shared IPD and supporting documents must be evaluated to ensure the potential for scientific advancements from the data sharing ecosystem can be realized.

Objective

To assess the utility and completeness of IPD and supporting documents provided from industry-sponsored clinical trials.

Design, Setting, and Participants

From February 9, 2022, to February 9, 2023, 91 of 203 clinical trials supporting US Food and Drug Administration registrations of anticancer medicines for the treatment of solid tumors from the past decade were confirmed as eligible for IPD request. This quality improvement study performed a retrospective audit of the utility and completeness of the IPD and supporting documents provided from the 91 clinical trials for a planned meta-analysis.

Exposures

Request for IPD from 91 clinical oncology trials indicated as eligible for the request.

Main Outcomes and Measures

The utility and completeness of the IPD and supporting documents provided.

Results

The IPD packages were obtained from 70 of 91 requested clinical trials (77%). The median time to data provision was 123 (range, 117-352) days. Redactions were observed in 18 of the acquired IPD packages (26%) for outcome data, 11 (16%) for assessment variables, and 19 (27%) for adjustment data. Additionally, 20 IPD packages (29%) lacked a clinical study report, 4 (6%) had incomplete or missing data dictionaries, and 20 (29%) were missing anonymization or redaction description files. Access to IPD from 21 eligible trials (23%) was not granted.

Conclusions and Relevance

In this quality improvement study, there was substantial variability within the provided IPD packages regarding the completeness of key data variables and supporting documents. To improve the data sharing ecosystem, key areas for enhancement include (1) ensuring that clinical trials are eligible for IPD sharing, (2) making eligible IPD transparently accessible, and (3) ensuring that IPD packages meet a standard of utility and completeness.

Addressing Systematic Missing Data in the Context of Causally Interpretable Meta-analysis

Evidence synthesis involves drawing conclusions from trial samples that may differ from the target population of interest, and there is often heterogeneity among trials in sample characteristics, treatment implementation, study design, and assessment of covariates. Stitching together this patchwork of evidence requires subject-matter knowledge, a clearly defined target population, and guidance on how to weigh evidence from different trials. Transportability analysis has provided formal identifiability conditions required to make unbiased causal inference in the target population. In this manuscript, we review these conditions along with an additional assumption required to address systematic missing data. The identifiability conditions highlight the importance of accounting for differences in treatment effect modifiers between the populations underlying the trials and the target population. We perform simulations to evaluate the bias of conventional random effect models and multiply imputed estimates using the pooled trials sample and describe causal estimators that explicitly address trial-to-target differences in key covariates in the context of systematic missing data. Results indicate that the causal transportability estimators are unbiased when treatment effect modifiers are accounted for in the analyses. Results also highlight the importance of carefully evaluating identifiability conditions for each trial to reduce bias due to differences in participant characteristics between trials and the target population. Bias can be limited by adjusting for covariates that are strongly correlated with missing treatment effect modifiers, including data from trials that do not differ from the target on treatment modifiers, and removing trials that do differ from the target and did not assess a modifier.

FAIR, ethical, and coordinated data sharing for COVID-19 response: a scoping review and cross-sectional survey of COVID-19 data sharing platforms and registries

Data sharing is central to the rapid translation of research into advances in clinical medicine and public health practice. In the context of COVID-19, there has been a rush to share data marked by an explosion of population-specific and discipline-specific resources for collecting, curating, and disseminating participant-level data. We conducted a scoping review and cross-sectional survey to identify and describe COVID-19-related platforms and registries that harmonise and share participant-level clinical, omics (eg, genomic and metabolomic data), imaging data, and metadata. We assess how these initiatives map to the best practices for the ethical and equitable management of data and the findable, accessible, interoperable, and reusable (FAIR) principles for data resources. We review gaps and redundancies in COVID-19 data-sharing efforts and provide recommendations to build on existing synergies that align with frameworks for effective and equitable data reuse. We identified 44 COVID-19-related registries and 20 platforms from the scoping review. Data-sharing resources were concentrated in high-income countries and siloed by comorbidity, body system, and data type. Resources for harmonising and sharing clinical data were less likely to implement FAIR principles than those sharing omics or imaging data. Our findings are that more data sharing does not equate to better data sharing, and the semantic and technical interoperability of platforms and registries harmonising and sharing COVID-19-related participant-level data needs to improve to facilitate the global collaboration required to address the COVID-19 crisis.

Privacy risks of whole-slide image sharing in digital pathology

Access to large volumes of so-called whole-slide images-high-resolution scans of complete pathological slides-has become a cornerstone of the development of novel artificial intelligence methods in pathology for diagnostic use, education/training of pathologists, and research. Nevertheless, a methodology based on risk analysis for evaluating the privacy risks associated with sharing such imaging data and applying the principle "as open as possible and as closed as necessary" is still lacking. In this article, we develop a model for privacy risk analysis for whole-slide images which focuses primarily on identity disclosure attacks, as these are the most important from a regulatory perspective. We introduce a taxonomy of whole-slide images with respect to privacy risks and mathematical model for risk assessment and design . Based on this risk assessment model and the taxonomy, we conduct a series of experiments to demonstrate the risks using real-world imaging data. Finally, we develop guidelines for risk assessment and recommendations for low-risk sharing of whole-slide image data.

Institutional dashboards on clinical trial transparency for University Medical Centers: A case study

BACKGROUND

University Medical Centers (UMCs) must do their part for clinical trial transparency by fostering practices such as prospective registration, timely results reporting, and open access. However, research institutions are often unaware of their performance on these practices. Baseline assessments of these practices would highlight where there is room for change and empower UMCs to support improvement. We performed a status quo analysis of established clinical trial registration and reporting practices at German UMCs and developed a dashboard to communicate these baseline assessments with UMC leadership and the wider research community.

METHODS AND FINDINGS

We developed and applied a semiautomated approach to assess adherence to established transparency practices in a cohort of interventional trials and associated results publications. Trials were registered in ClinicalTrials.gov or the German Clinical Trials Register (DRKS), led by a German UMC, and reported as complete between 2009 and 2017. To assess adherence to transparency practices, we identified results publications associated to trials and applied automated methods at the level of registry data (e.g., prospective registration) and publications (e.g., open access). We also obtained summary results reporting rates of due trials registered in the EU Clinical Trials Register (EUCTR) and conducted at German UMCs from the EU Trials Tracker. We developed an interactive dashboard to display these results across all UMCs and at the level of single UMCs. Our study included and assessed 2,895 interventional trials led by 35 German UMCs. Across all UMCs, prospective registration increased from 33% (n = 58/178) to 75% (n = 144/193) for trials registered in ClinicalTrials.gov and from 0% (n = 0/44) to 79% (n = 19/24) for trials registered in DRKS over the period considered. Of trials with a results publication, 38% (n = 714/1,895) reported the trial registration number in the publication abstract. In turn, 58% (n = 861/1,493) of trials registered in ClinicalTrials.gov and 23% (n = 111/474) of trials registered in DRKS linked the publication in the registration. In contrast to recent increases in summary results reporting of drug trials in the EUCTR, 8% (n = 191/2,253) and 3% (n = 20/642) of due trials registered in ClinicalTrials.gov and DRKS, respectively, had summary results in the registry. Across trial completion years, timely results reporting (within 2 years of trial completion) as a manuscript publication or as summary results was 41% (n = 1,198/2,892). The proportion of openly accessible trial publications steadily increased from 42% (n = 16/38) to 74% (n = 72/97) over the period considered. A limitation of this study is that some of the methods used to assess the transparency practices in this dashboard rely on registry data being accurate and up-to-date.

CONCLUSIONS

In this study, we observed that it is feasible to assess and inform individual UMCs on their performance on clinical trial transparency in a reproducible and publicly accessible way. Beyond helping institutions assess how they perform in relation to mandates or their institutional policy, the dashboard may inform interventions to increase the uptake of clinical transparency practices and serve to evaluate the impact of these interventions.

Qualitative data sharing practices in clinical trials in the UK and Ireland: towards the production of good practice guidance

Background: Data sharing enables researchers to conduct novel research with previously collected datasets, thus maximising scientific findings and cost effectiveness, and reducing research waste. The value of sharing, even de-identified, quantitative data from clinical trials is well recognised with a moderated access approach recommended. While substantial challenges to sharing quantitative data remain, there are additional challenges for sharing qualitative data in trials. Incorporating the necessary information about how qualitative data will be shared into already complex trial recruitment and consent processes proves challenging. The aim of this study was to explore whether and how trial teams share qualitative data collected as part of the design, conduct, analysis, or delivery of clinical trials. Methods: Phase 1 involved semi-structured, in-depth qualitative interviews and focus groups with key trial stakeholder groups including trial managers and clinical trialists (n=3), qualitative researchers in trials (n=9), members of research funding bodies (n=2) and trial participants (n=1). Data were analysed using thematic analysis. In Phase 2, we conducted a content analysis of 16 participant information leaflets (PIL) and consent forms (CF) for trials that collected qualitative data. Results: Three key themes were identified from our Phase 1 findings: ' Understanding and experiences of the potential benefits of sharing qualitative data from trials', 'Concerns about qualitative data sharing', and ' Future guidance and funding'. In phase 2, the PILs and CFs received revealed that the benefits of data sharing for participants were only explained in two of the study documents. Conclusions: The value of sharing qualitative data was acknowledged, but there are many uncertainties as to how, when, and where to share this data. In addition, there were ethical concerns in relation to the consent process required for qualitative data sharing in trials. This study provides insight into the existing practice of qualitative data sharing in trials.

Consent Codes: Maintaining Consent in an Ever-expanding Open Science Ecosystem

We previously proposed a structure for recording consent-based data use 'categories' and 'requirements' - Consent Codes - with a view to supporting maximum use and integration of genomic research datasets, and reducing uncertainty about permissible re-use of shared data. Here we discuss clarifications and subsequent updates to the Consent Codes (v4) based on new areas of application (e.g., the neurosciences, biobanking, H3Africa), policy developments (e.g., return of research results), and further practical considerations, including developments in automated approaches to consent management.

Data and sample sharing as an enabler for large-scale biomarker research and development: The EPND perspective

Biomarker discovery, development, and validation are reliant on large-scale analyses of high-quality samples and data. Currently, significant quantities of data and samples have been generated by European studies on Alzheimer's disease (AD) and other neurodegenerative diseases (NDD), representing a valuable resource for developing biomarkers to support early detection of disease, treatment monitoring, and patient stratification. However, discovery of, access to, and sharing of data and samples from AD and NDD research are hindered both by silos that limit collaboration, and by the array of complex requirements for secure, legal, and ethical sharing. In this Perspective article, we examine key challenges currently hampering large-scale biomarker research, and outline how the European Platform for Neurodegenerative Diseases (EPND) plans to address them. The first such challenge is a fragmented landscape filled with technical barriers that make it difficult to discover and access high-quality samples and data in one location. A second challenge is related to the complex array of legal and ethical requirements that must be navigated by researchers when sharing data and samples, to ensure compliance with data protection regulations and research ethics. Another challenge is the lack of broad-scale collaboration and opportunities to facilitate partnerships between data and sample contributors and researchers, in addition to a lack of regulatory engagement early in the research process to enable validation of potential biomarkers. A further challenge facing projects is the need to remain sustainable beyond initial funding periods, ensuring data and samples are shared and reused, thereby driving further research and innovation. In addressing these challenges, EPND will enable an environment of faster and more disruptive research on diagnostics and disease-modifying therapies for Alzheimer's disease and other neurodegenerative diseases.

Data sharing and community-engaged research

Data sharing must be accompanied by responsibility sharing.

Early termination of pivotal trials in Alzheimer's disease-Preserving optimal value for participants and science

Participants in Alzheimer's disease late-phase clinical trials are frequently confronted with a situation of early termination. We discuss measures to protect the perceived value of study participation and to maximize the scientific value under such circumstances. A communication strategy should ensure that trial participants maintain a positive relationship with the research team and have their informational needs optimally met. Measures to maximize the scientific value may include data/sample sharing, strategies for personalized medicine, as well as scientific follow-up. Critical for the success of such a concept are networks of excellence, extending models of existing initiatives like Global Alzheimer's Platform Foundation Network (GAP-Net). These networks could fundamentally strengthen the role of clinical investigators if they decide on their involvement in trials based upon their estimation of the scientific value and benefit for the participants, actively contribute to scientific analyses, and mediate optimal communication among the relevant trial stakeholders.

Audit of Data Sharing by Pharmaceutical Companies for Anticancer Medicines Approved by the US Food and Drug Administration

Question

What proportion of clinical trials that underpin registration of contemporary anticancer medicines are eligible for individual participant data (IPD) sharing with qualified researchers?

Findings

In this quality improvement study of the 304 trials that underpinned the US Food and Drug Administration (FDA) registration of 115 anticancer medicines over the past 10 years, 136 (45%) were eligible for IPD sharing.

Meaning

Although inroads have been made toward improving IPD transparency over the past 5 years, these findings suggest that a substantial portion of pivotal oncology trials that support the FDA registration of modern anticancer medicines remain unavailable to qualified researchers.

Importance

Emerging policies drafted by the pharmaceutical industry indicate that they will transparently share clinical trial data. These data offer an unparalleled opportunity to advance evidence-based medicine and support decision-making.

Objective

To evaluate the eligibility of independent, qualified researchers to access individual participant data (IPD) from oncology trials that supported US Food and Drug Administration (FDA) approval of new anticancer medicines within the past 10 years.

Design, Setting, and Participants

In this quality improvement study, a cross-sectional analysis was performed of pivotal clinical trials whose results supported FDA-approved anticancer medicines between January 1, 2011, and June 30, 2021. These trials’ results were identified from product labels.

Exposures

Eligibility for IPD sharing was confirmed by identification of a public listing of the trial as eligible for sharing or by receipt of a positive response from the sponsor to a standardized inquiry.

Main Outcomes and Measures

The main outcome was frequency of IPD sharing eligibility. Reasons for data sharing ineligibility were requested and collated, and company-, drug-, and trial-level subgroups were evaluated and presented using χ² tests and forest plots.

Results

During the 10-year period examined, 115 anticancer medicines were approved by the FDA on the basis of evidence from 304 pharmaceutical industry–sponsored trials. Of these trials, 136 (45%) were eligible for IPD sharing and 168 (55%) were not. Data sharing rates differed substantially among industry sponsors, with the most common reason for not sharing trial IPD being that the collection of long-term follow-up data was still ongoing (89 of 168 trials [53%]). Of the top 10 anticancer medicines by global sales, nivolumab, pembrolizumab, and pomalidomide had the lowest eligibility rates for data sharing (<10% of trials).

Conclusions and Relevance

There has been a substantial increase in IPD sharing for industry-sponsored oncology trials over the past 5 years. However, this quality improvement study found that more than 50% of queried trials for FDA-approved anticancer medicines were ineligible for IPD sharing. Data accessibility would be substantially improved if, at the time of FDA registration of a medicine, all data that support the registration were made available.

Current recommendations/practices for anonymising data from clinical trials in order to make it available for sharing: A scoping review

Background/Aims

There are increasing pressures for anonymised datasets from clinical trials to be shared across the scientific community, and differing recommendations exist on how to perform anonymisation prior to sharing. We aimed to systematically identify, describe and synthesise existing recommendations for anonymising clinical trial datasets to prepare for data sharing.

Methods

We systematically searched MEDLINE^®, EMBASE and Web of Science from inception to 8 February 2021. We also searched other resources to ensure the comprehensiveness of our search. Any publication reporting recommendations on anonymisation to enable data sharing from clinical trials was included. Two reviewers independently screened titles, abstracts and full text for eligibility. One reviewer extracted data from included papers using thematic synthesis, which then was sense-checked by a second reviewer. Results were summarised by narrative analysis.

Results

Fifty-nine articles (from 43 studies) were eligible for inclusion. Three distinct themes are emerging: anonymisation, de-identification and pseudonymisation. The most commonly used anonymisation techniques are: removal of direct patient identifiers; and careful evaluation and modification of indirect identifiers to minimise the risk of identification. Anonymised datasets joined with controlled access was the preferred method for data sharing.

Conclusions

There is no single standardised set of recommendations on how to anonymise clinical trial datasets for sharing. However, this systematic review shows a developing consensus on techniques used to achieve anonymisation. Researchers in clinical trials still consider that anonymisation techniques by themselves are insufficient to protect patient privacy, and they need to be paired with controlled access.

Regulatory Frameworks for Clinical Trial Data Sharing: Scoping Review

Background

Although well recognized for its scientific value, data sharing from clinical trials remains limited. Steps toward harmonization and standardization are increasing in various pockets of the global scientific community. This issue has gained salience during the COVID-19 pandemic. Even for agencies willing to share data, data exclusivity practices complicate matters; strict regulations by funders affect this even further. Finally, many low- and middle-income countries (LMICs) have weaker institutional mechanisms. This complex of factors hampers research and rapid response during public health emergencies. This drew our attention to the need for a review of the regulatory landscape governing clinical trial data sharing.

Objective

This review seeks to identify regulatory frameworks and policies that govern clinical trial data sharing and explore key elements of data-sharing mechanisms as outlined in existing regulatory documents. Following from, and based on, this empirical analysis of gaps in existing policy frameworks, we aimed to suggest focal areas for policy interventions on a systematic basis to facilitate clinical trial data sharing.

Methods

We followed the JBI scoping review approach. Our review covered electronic databases and relevant gray literature through a targeted web search. We included records (all publication types, except for conference abstracts) available in English that describe clinical trial data–sharing policies, guidelines, or standard operating procedures. Data extraction was performed independently by 2 authors, and findings were summarized using a narrative synthesis approach.

Results

We identified 4 articles and 13 policy documents; none originated from LMICs. Most (11/17, 65%) of the clinical trial agencies mandated a data-sharing agreement; 47% (8/17) of these policies required informed consent by trial participants; and 71% (12/17) outlined requirements for a data-sharing proposal review committee. Data-sharing policies have, a priori, milestone-based timelines when clinical trial data can be shared. We classify clinical trial agencies as following either controlled- or open-access data-sharing models. Incentives to promote data sharing and distinctions between mandated requirements and supportive requirements for informed consent during the data-sharing process remain gray areas, needing explication. To augment participant privacy and confidentiality, a neutral institutional mechanism to oversee dissemination of information from the appropriate data sets and more policy interventions led by LMICs to facilitate data sharing are strongly recommended.

Conclusions

Our review outlines the immediate need for developing a pragmatic data-sharing mechanism that aims to improve research and innovations as well as facilitate cross-border collaborations. Although a one-policy-fits-all approach would not account for regional and subnational legislation, we suggest that a focus on key elements of data-sharing mechanisms can be used to inform the development of flexible yet comprehensive data-sharing policies so that institutional mechanisms rather than disparate efforts guide data generation, which is the foundation of all scientific endeavor.

Individual participant data (IPD)-level meta-analysis of randomised controlled trials to estimate the vitamin D dietary requirements in dark-skinned individuals resident at high latitude

CONTEXT AND PURPOSE

There is an urgent need to develop vitamin D dietary recommendations for dark-skinned populations resident at high latitude. Using data from randomised controlled trials (RCTs) with vitamin D₃-supplements/fortified foods, we undertook an individual participant data-level meta-regression (IPD) analysis of the response of wintertime serum 25-hydroxyvitamin (25(OH)D) to total vitamin D intake among dark-skinned children and adults residing at ≥ 40° N and derived dietary requirement values for vitamin D.

METHODS

IPD analysis using data from 677 dark-skinned participants (of Black or South Asian descent; ages 5-86 years) in 10 RCTs with vitamin D supplements/fortified foods identified via a systematic review and predefined eligibility criteria. Outcome measures were vitamin D intake estimates across a range of 25(OH)D thresholds.

RESULTS

To maintain serum 25(OH)D concentrations ≥ 25 and 30 nmol/L in 97.5% of individuals, 23.9 and 27.3 µg/day of vitamin D, respectively, were required among South Asian and 24.1 and 33.2 µg/day, respectively, among Black participants. Overall, our age-stratified intake estimates did not exceed age-specific Tolerable Upper Intake Levels for vitamin D. The vitamin D intake required by dark-skinned individuals to maintain 97.5% of winter 25(OH)D concentrations ≥ 50 nmol/L was 66.8 µg/day. This intake predicted that the upper 2.5% of individuals could potentially achieve serum 25(OH)D concentrations ≥ 158 nmol/L, which has been linked to potential adverse effects in older adults in supplementation studies.

CONCLUSIONS

Our IPD-derived vitamin D intakes required to maintain 97.5% of winter 25(OH)D concentrations ≥ 25, 30 and 50 nmol/L are substantially higher than the equivalent estimates for White individuals. These requirement estimates are also higher than those currently recommended internationally by several agencies, which are based predominantly on data from Whites and derived from standard meta-regression based on aggregate data. Much more work is needed in dark-skinned populations both in the dose-response relationship and risk characterisation for health outcomes.

TRAIL REGISTRATION

PROSPERO International Prospective Register of Systematic Reviews (Registration Number: CRD42018097260).

How Can a Clinical Data Modelling Tool Be Used to Represent Data Items of Relevance to Paediatric Clinical Trials? Learning from the Conect4children (c4c) Consortium

Data dictionaries for clinical trials are often created manually, with data structures and controlled vocabularies specific for a trial or family of trials within a sponsor’s portfolio. Microsoft Excel is commonly used to capture the representation of data dictionary items but has limited functionality for this purpose. The conect4children (c4c) network is piloting the Direcht clinical data modelling tool to model their Cross Cutting Paediatric Data Dictionary (CCPDD) in a more formalised way. The first pilot had the key objective of testing whether a clinical data modelling tool could be used to represent data items from the CCPDD. The key objective of the second pilot is to establish whether a small team with little or no experience of clinical data modelling can use Direcht to expand the CCPDD. Clinical modelling is the process of structuring clinical data so it can be understood by computer systems and humans. The model contains all of the elements that are needed to define the data item. Results from the pilots show that Direcht creates a structured environment to build data items into models that fit into the larger CCPDD. Models can be represented as an HTML document, mind map, or exported in various formats for import into a computer system. Challenges identified over the course of both pilots are being addressed with c4c partners and external stakeholders.

GA4GH: International policies and standards for data sharing across genomic research and healthcare

The Global Alliance for Genomics and Health (GA4GH) aims to accelerate biomedical advances by enabling the responsible sharing of clinical and genomic data through both harmonized data aggregation and federated approaches. The decreasing cost of genomic sequencing (along with other genome-wide molecular assays) and increasing evidence of its clinical utility will soon drive the generation of sequence data from tens of millions of humans, with increasing levels of diversity. In this perspective, we present the GA4GH strategies for addressing the major challenges of this data revolution. We describe the GA4GH organization, which is fueled by the development efforts of eight Work Streams and informed by the needs of 24 Driver Projects and other key stakeholders. We present the GA4GH suite of secure, interoperable technical standards and policy frameworks and review the current status of standards, their relevance to key domains of research and clinical care, and future plans of GA4GH. Broad international participation in building, adopting, and deploying GA4GH standards and frameworks will catalyze an unprecedented effort in data sharing that will be critical to advancing genomic medicine and ensuring that all populations can access its benefits.

Pediatric Cancer Data Commons: Federating and Democratizing Data for Childhood Cancer Research

The international pediatric oncology community has a long history of research collaboration. In the United States, the 2019 launch of the Children's Cancer Data Initiative puts the focus on developing a rich and robust data ecosystem for pediatric oncology. In this spirit, we present here our experience in constructing the Pediatric Cancer Data Commons (PCDC) to highlight the significance of this effort in fighting pediatric cancer and improving outcomes and to provide essential information to those creating resources in other disease areas. The University of Chicago's PCDC team has worked with the international research community since 2015 to build data commons for children's cancers. We identified six critical features of successful data commons design and implementation: (1) establish the need for a data commons, (2) develop and deploy the technical infrastructure, (3) establish and implement governance, (4) make the data commons platform easy and intuitive for researchers, (5) socialize the data commons and create working knowledge and expertise in the research community, and (6) plan for longevity and sustainability. Data commons are critical to conducting research on large patient cohorts that will ultimately lead to improved outcomes for children with cancer. There is value in connecting high-quality clinical and phenotype data to external sources of data such as genomic, proteomics, and imaging data. Next steps for the PCDC include creating an informed and invested data-sharing culture, developing sustainable methods of data collection and sharing, standardizing genetic biomarker reporting, incorporating radiologic and molecular analysis data, and building models for electronic patient consent. The methods and processes described here can be extended to any clinical area and provide a blueprint for others wishing to develop similar resources.

Clinical Implications of Body Mass Index in Metastatic Breast Cancer Patients Treated With Abemaciclib and Endocrine Therapy

BACKGROUND

There are limited data regarding the impact of body mass index (BMI) on outcomes in advanced breast cancer, especially in patients treated with endocrine therapy (ET) + cyclin-dependent kinase 4/6 inhibitors.

METHODS

A pooled analysis of individual patient-level data from MONARCH 2 and 3 trials was performed. Patients were classified according to baseline BMI into underweight (<18.5 kg/m2), normal (18.5-24.9 kg/m2), overweight (25-29.9 kg/m2), and obese (≥30 kg/m2) and divided into 2 treatment groups: abemaciclib + ET vs placebo + ET. The primary endpoint was progression-free survival (PFS) according to BMI in each treatment group. Secondary endpoints were response rate, adverse events according to BMI, and loss of weight (≥5% from baseline) during treatment.

RESULTS

This analysis included 1138 patients (757 received abemaciclib + ET and 381 placebo + ET). There was no difference in PFS between BMI categories in either group, although normal-weight patients presented a numerically higher benefit with abemaciclib + ET (Pinteraction = .07). Normal and/or underweight patients presented higher overall response rate in the abemaciclib + ET group compared with overweight and/or obese patients (49.4% vs 41.6%, odds ratio = 0.73, 95% confidence interval = 0.54 to 0.99) as well as higher neutropenia frequency (51.0% vs 40.4%, P = .004). Weight loss was more frequent in the abemaciclib + ET group (odds ratio = 3.23, 95% confidence interval = 2.09 to 5.01).

CONCLUSIONS

Adding abemaciclib to ET prolongs PFS regardless of BMI, showing that overweight or obese patients also benefit from this regimen. Our results elicit the possibility of a better effect of abemaciclib in normal and/or underweight patients compared with overweight and/or obese patients. More studies analyzing body composition parameters in patients under treatment with cyclin-dependent kinase 4/6 inhibitors may further clarify this hypothesis.

De-novo FAIRification via an Electronic Data Capture system by automated transformation of filled electronic Case Report Forms into machine-readable data

INTRODUCTION

Existing methods to make data Findable, Accessible, Interoperable, and Reusable (FAIR) are usually carried out in a post hoc manner: after the research project is conducted and data are collected. De-novo FAIRification, on the other hand, incorporates the FAIRification steps in the process of a research project. In medical research, data is often collected and stored via electronic Case Report Forms (eCRFs) in Electronic Data Capture (EDC) systems. By implementing a de novo FAIRification process in such a system, the reusability and, thus, scalability of FAIRification across research projects can be greatly improved. In this study, we developed and implemented a novel method for de novo FAIRification via an EDC system. We evaluated our method by applying it to the Registry of Vascular Anomalies (VASCA).

METHODS

Our EDC and research project independent method ensures that eCRF data entered into an EDC system can be transformed into machine-readable, FAIR data using a semantic data model (a canonical representation of the data, based on ontology concepts and semantic web standards) and mappings from the model to questions on the eCRF. The FAIRified data are stored in a triple store and can, together with associated metadata, be accessed and queried through a FAIR Data Point. The method was implemented in Castor EDC, an EDC system, through a data transformation application. The FAIRness of the output of the method, the FAIRified data and metadata, was evaluated using the FAIR Evaluation Services.

RESULTS

We successfully applied our FAIRification method to the VASCA registry. Data entered on eCRFs is automatically transformed into machine-readable data and can be accessed and queried using SPARQL queries in the FAIR Data Point. Twenty-one FAIR Evaluator tests pass and one test regarding the metadata persistence policy fails, since this policy is not in place yet.

CONCLUSION

In this study, we developed a novel method for de novo FAIRification via an EDC system. Its application in the VASCA registry and the automated FAIR evaluation show that the method can be used to make clinical research data FAIR when they are entered in an eCRF without any intervention from data management and data entry personnel. Due to the generic approach and developed tooling, we believe that our method can be used in other registries and clinical trials as well.

Characteristics of available studies and dissemination of research using major clinical data sharing platforms

BACKGROUND/AIMS

Over the past decade, numerous data sharing platforms have been launched, providing access to de-identified individual patient-level data and supporting documentation. We evaluated the characteristics of prominent clinical data sharing platforms, including types of studies listed as available for request, data requests received, and rates of dissemination of research findings from data requests.

METHODS

We reviewed publicly available information listed on the websites of six prominent clinical data sharing platforms: Biological Specimen and Data Repository Information Coordinating Center, ClinicalStudyDataRequest.com, Project Data Sphere, Supporting Open Access to Researchers-Bristol Myers Squibb, Vivli, and the Yale Open Data Access Project. We recorded key platform characteristics, including listed studies and available supporting documentation, information on the number and status of data requests, and rates of dissemination of research findings from data requests (i.e. publications in a peer-reviewed journals, preprints, conference abstracts, or results reported on the platform's website).

RESULTS

The number of clinical studies listed as available for request varied among five data sharing platforms: Biological Specimen and Data Repository Information Coordinating Center (n = 219), ClinicalStudyDataRequest.com (n = 2,897), Project Data Sphere (n = 154), Vivli (n = 5426), and the Yale Open Data Access Project (n = 395); Supporting Open Access to Researchers did not provide a list of Bristol Myers Squibb studies available for request. Individual patient-level data were nearly always reported as being available for request, as opposed to only Clinical Study Reports (Biological Specimen and Data Repository Information Coordinating Center = 211/219 (96.3%); ClinicalStudyDataRequest.com = 2884/2897 (99.6%); Project Data Sphere = 154/154 (100.0%); and the Yale Open Data Access Project = 355/395 (89.9%)); Vivli did not provide downloadable study metadata. Of 1201 data requests listed on ClinicalStudyDataRequest.com, Supporting Open Access to Researchers-Bristol Myers Squibb, Vivli, and the Yale Open Data Access Project platforms, 586 requests (48.8%) were approved (i.e. data access granted). The majority were for secondary analyses and/or developing/validating methods (ClinicalStudyDataRequest.com = 262/313 (83.7%); Supporting Open Access to Researchers-Bristol Myers Squibb = 22/30 (73.3%); Vivli = 63/84 (75.0%); the Yale Open Data Access Project = 111/159 (69.8%)); four were for re-analyses or corroborations of previous research findings (ClinicalStudyDataRequest.com = 3/313 (1.0%) and the Yale Open Data Access Project = 1/159 (0.6%)). Ninety-five (16.1%) approved data requests had results disseminated via peer-reviewed publications (ClinicalStudyDataRequest.com = 61/313 (19.5%); Supporting Open Access to Researchers-Bristol Myers Squibb = 3/30 (10.0%); Vivli = 4/84 (4.8%); the Yale Open Data Access Project = 27/159 (17.0%)). Forty-two (6.8%) additional requests reported results through preprints, conference abstracts, or on the platform's website (ClinicalStudyDataRequest.com = 12/313 (3.8%); Supporting Open Access to Researchers-Bristol Myers Squibb = 3/30 (10.0%); Vivli = 2/84 (2.4%); Yale Open Data Access Project = 25/159 (15.7%)).

CONCLUSION

Across six prominent clinical data sharing platforms, information on studies and request metrics varied in availability and format. Most data requests focused on secondary analyses and approximately one-quarter of all approved requests publicly disseminated their results. To further promote the use of shared clinical data, platforms should increase transparency, consistently clarify the availability of the listed studies and supporting documentation, and ensure that research findings from data requests are disseminated.

Status, use and impact of sharing individual participant data from clinical trials: a scoping review

OBJECTIVES

To explore the impact of data-sharing initiatives on the intent to share data, on actual data sharing, on the use of shared data and on research output and impact of shared data.

ELIGIBILITY CRITERIA

All studies investigating data-sharing practices for individual participant data (IPD) from clinical trials.

SOURCES OF EVIDENCE

We searched the Medline database, the Cochrane Library, the Science Citation Index Expanded and the Social Sciences Citation Index via Web of Science, and preprints and proceedings of the International Congress on Peer Review and Scientific Publication. In addition, we inspected major clinical trial data-sharing platforms, contacted major journals/publishers, editorial groups and some funders.

CHARTING METHODS

Two reviewers independently extracted information on methods and results from resources identified using a standardised questionnaire. A map of the extracted data was constructed and accompanied by a narrative summary for each outcome domain.

RESULTS

93 studies identified in the literature search (published between 2001 and 2020, median: 2018) and 5 from additional information sources were included in the scoping review. Most studies were descriptive and focused on early phases of the data-sharing process. While the willingness to share IPD from clinical trials is extremely high, actual data-sharing rates are suboptimal. A survey of journal data suggests poor to moderate enforcement of the policies by publishers. Metrics provided by platforms suggest that a large majority of data remains unrequested. When requested, the purpose of the reuse is more often secondary analyses and meta-analyses, rarely re-analyses. Finally, studies focused on the real impact of data-sharing were rare and used surrogates such as citation metrics.

CONCLUSIONS

There is currently a gap in the evidence base for the impact of IPD sharing, which entails uncertainties in the implementation of current data-sharing policies. High level evidence is needed to assess whether the value of medical research increases with data-sharing practices.

PRIME-IPD SERIES Part 1. The PRIME-IPD tool promoted verification and standardization of study datasets retrieved for IPD meta-analysis

OBJECTIVES

We describe a systematic approach to preparing data in the conduct of Individual Participant Data (IPD) analysis.

STUDY DESIGN AND SETTING

A guidance paper proposing methods for preparing individual participant data for meta-analysis from multiple study sources, developed by consultation of relevant guidance and experts in IPD. We present an example of how these steps were applied in checking data for our own IPD meta analysis (IPD-MA).

RESULTS

We propose five steps of Processing, Replication, Imputation, Merging, and Evaluation to prepare individual participant data for meta-analysis (PRIME-IPD). Using our own IPD-MA as an exemplar, we found that this approach identified missing variables and potential inconsistencies in the data, facilitated the standardization of indicators across studies, confirmed that the correct data were received from investigators, and resulted in a single, verified dataset for IPD-MA.

CONCLUSION

The PRIME-IPD approach can assist researchers to systematically prepare, manage and conduct important quality checks on IPD from multiple studies for meta-analyses. Further testing of this framework in IPD-MA would be useful to refine these steps.

Minimally Invasive Glaucoma Surgery: A Critical Appraisal of the Literature

Micro- or minimally invasive glaucoma surgeries (MIGS) have been the latest addition to the glaucoma surgical treatment paradigm. This term refers not to a single surgery, but rather to a group of distinct procedures and devices that aim to decrease intraocular pressure. Broadly, MIGS can be categorized into surgeries that increase the trabecular outflow [Trabectome, iStent (first and second generations), Hydrus microstent, Kahook Dual Blade and gonioscopy-assisted transluminal trabeculotomy], surgeries that increase suprachoroidal outflow (Cypass microstent and iStent Supra), and conjunctival bleb-forming procedures (Xen gel stent and InnFocus microshunt). Compared to traditional glaucoma surgeries, such as trabeculectomy and glaucoma drainage device implantation (Ahmed, Baerveldt, and Molteno valves), MIGS are touted to have less severe complications and shorter surgical time. MIGS represent an evolving field, and the efficacy and complications of each procedure should be considered independently, giving more importance to high-quality and longer-term studies.

Ensuring Prevention Science Research is Synthesis-Ready for Immediate and Lasting Scientific Impact

When seeking to inform and improve prevention efforts and policy, it is important to be able to robustly synthesize all available evidence. But evidence sources are often large and heterogeneous, so understanding what works, for whom, and in what contexts can only be achieved through a systematic and comprehensive synthesis of evidence. Many barriers impede comprehensive evidence synthesis, which leads to uncertainty about the generalizability of intervention effectiveness, including inaccurate titles/abstracts/keywords terminology (hampering literature search efforts), ambiguous reporting of study methods (resulting in inaccurate assessments of study rigor), and poorly reported participant characteristics, outcomes, and key variables (obstructing the calculation of an overall effect or the examination of effect modifiers). To address these issues and improve the reach of primary studies through their inclusion in evidence syntheses, we provide a set of practical guidelines to help prevention scientists prepare synthesis-ready research. We use a recent mindfulness trial as an empirical example to ground the discussion and demonstrate ways to ensure the following: (1) primary studies are discoverable; (2) the types of data needed for synthesis are present; and (3) these data are readily synthesizable. We highlight several tools and practices that can aid authors in these efforts, such as using a data-driven approach for crafting titles, abstracts, and keywords or by creating a repository for each project to host all study-related data files. We also provide step-by-step guidance and software suggestions for standardizing data design and public archiving to facilitate synthesis-ready research.

The CORBEL matrix on informed consent in clinical studies: a multidisciplinary approach of Research Infrastructures Building Enduring Life-science Services

BACKGROUND

Informed consent forms for clinical research are several and variable at international, national and local levels. According to the literature, they are often unclear and poorly understood by participants. Within the H2020 project CORBEL-Coordinated Research Infrastructures Building Enduring Life-science Services-clinical researchers, researchers in ethical, social, and legal issues, experts in planning and management of clinical studies, clinicians, researchers in citizen involvement and public engagement worked together to provide a minimum set of requirements for informed consent in clinical studies.

METHODS

The template was based on a literature review including systematic reviews and guidelines searched on PubMed, Embase, Cochrane Library, NICE, SIGN, GIN, and Clearinghouse databases, and on comparison of templates gathered through an extensive search on the websites of research institutes, national and international agencies, and international initiatives. We discussed the draft versions step-by-step and then we referred to it as the "matrix" to underline its modular character and indicate that it allows adaptation to the context in which it will be used. The matrix was revised by representatives of two international patient groups.

RESULTS

The matrix covers the process of ensuring that the appropriate information, context and setting are provided so that the participant can give truly informed consent. It addresses the key topics and proposes wording on how to clarify the meaning of placebo and of non-inferiority studies, the importance of individual participants' data sharing, and the impossibility of knowing in advance how the data might be used in future studies. Finally, it presents general suggestions on wording, format, and length of the information sheet.

CONCLUSIONS

The matrix underlines the importance of improving the process of communication, its proper conditions (space, time, setting), and addresses the participants' lack of knowledge on how clinical research is conducted. It can be easily applied to a specific setting and could be a useful tool to identify the appropriate informed consent format for any study. The matrix is mainly intended to support multicentre interventional randomized clinical studies, but several suggestions also apply to non-interventional research.

Causally Interpretable Meta-analysis: Application in Adolescent HIV Prevention

Endowing meta-analytic results with a causal interpretation is challenging when there are differences in the distribution of effect modifiers among the populations underlying the included trials and the target population where the results of the meta-analysis will be applied. Recent work on transportability methods has described identifiability conditions under which the collection of randomized trials in a meta-analysis can be used to draw causal inferences about the target population. When the conditions hold, the methods enable estimation of causal quantities such as the average treatment effect and conditional average treatment effect in target populations that differ from the populations underlying the trial samples. The methods also facilitate comparison of treatments not directly compared in a head-to-head trial and assessment of comparative effectiveness within subgroups of the target population. We briefly describe these methods and present a worked example using individual participant data from three HIV prevention trials among adolescents in mental health care. We describe practical challenges in defining the target population, obtaining individual participant data from included trials and a sample of the target population, and addressing systematic missing data across datasets. When fully realized, methods for causally interpretable meta-analysis can provide decision-makers valid estimates of how treatments will work in target populations of substantive interest as well as in subgroups of these populations.

A study protocol of qualitative data sharing practices in clinical trials in the UK and Ireland: towards the production of good practice guidance

Background: Data sharing enables researchers to conduct novel research with previously collected data sets, thus maximising scientific findings and cost effectiveness, and reducing research waste. The value of sharing anonymised data from clinical trials is well recognised with a moderated access approach recommended. While substantial challenges to data sharing remain, there are additional challenges for qualitative data. Qualitative data including videos, interviews, and observations are often more readily identifiable than quantitative data. Existing guidance from UK Economic and Social Research Council applies to sharing qualitative data but does not address the additional challenges related to sharing qualitative data collected within trials, including the need to incorporate the necessary information and consent into already complex recruitment processes, with the additional sensitive nature of health-related data. Methods: Work package 1 will involve separate focus group interviews with members of each stakeholder group: trial managers, clinical trialists, qualitative researchers, members of research funding bodies and trial participants who have been involved in qualitative research. Data will be analysed using thematic analysis and managed within QSR NVivo to enhance transparency. Work package 2 will involve a documentary analysis of current consent procedures for qualitative data collected as part of the conduct of clinical trials. We will include documents such as participant information leaflets and consent forms for the qualitative components in trials. We will extract data such as whether specific clauses for data sharing are included in the consent form. Content analysis will be used to analyse whether and how consent is being obtained for qualitative data sharing. Conclusions: This study will provide insight into the existing practice of sharing of qualitative data in clinical trials and the current issues and opportunities, to help shape future research and development of guidance to encourage maximum learning to be gained from this valuable data.

Data sharing-trialists' plans at registration, attitudes, barriers and facilitators: A cohort study and cross-sectional survey

Data unavailability impedes research transparency and is a major problem for individual participant data (IPD) meta-analyses as it reduces statistical power, increases risk of bias, and may even preclude completion. The primary objectives of this study were to determine IPD sharing plans reported in recently registered clinical trial registration records, how data sharing commitment relates to clinical trial characteristics, and principal investigators' attitudes, motivations and barriers to data sharing. The secondary objective was to derive recommendations to overcome identified barriers to data sharing. This was a retrospective cohort study of all interventional trials registered on the Australian New Zealand Clinical Trials Registry (ANZCTR) from 1 December 2018 to 30 November 2019, and an online cross-sectional survey of their principal investigators. In the cohort study of all clinical trials registered on the ANZCTR in the study period (n = 1517), commitment to share data was low (22%, 329/1517). In the cross-sectional survey (n = 281, 23% response rate), principal investigators showed strong support for the concept of data sharing (77%, 216/281) but a substantially lower intention to actually share data from their clinical trials (40%, 111/281). Major barriers to data sharing included lacking informed consent to share data, protecting participant confidentiality and preventing misinterpretation of data or misleading secondary analyses. There is a gap between high in-principle support for data sharing, and low in-practice intention from investigators to share data from their own clinical trials. Multiple pathways exist to bridge this gap by addressing the identified barriers to data sharing.

Social media attention and citations of published outputs from re-use of clinical trial data: a matched comparison with articles published in the same journals

BACKGROUND

Data-sharing policies in randomized clinical trials (RCTs) should have an evaluation component. The main objective of this case-control study was to assess the impact of published re-uses of RCT data in terms of media attention (Altmetric) and citation rates.

METHODS

Re-uses of RCT data published up to December 2019 (cases) were searched for by two reviewers on 3 repositories (CSDR, YODA project, and Vivli) and matched to control papers published in the same journal. The Altmetric Attention Score (primary outcome), components of this score (e.g. mention of policy sources, media attention) and the total number of citations were compared between these two groups.

RESULTS

89 re-uses were identified: 48 (53.9%) secondary analyses, 34 (38.2%) meta-analyses, 4 (4.5%) methodological analyses and 3 (3.4%) re-analyses. The median (interquartile range) Altmetric Attention Scores were 5.9 (1.3-22.2) for re-use and 2.8 (0.3-12.3) for controls (p = 0.14). No statistical difference was found on any of the components of in the Altmetric Attention Score. The median (interquartile range) numbers of citations were 3 (1-8) for reuses and 4 (1 - 11.5) for controls (p = 0.30). Only 6/89 re-uses (6.7%) were cited in a policy source.

CONCLUSIONS

Using all available re-uses of RCT data to date from major data repositories, we were not able to demonstrate that re-uses attracted more attention than a matched sample of studies published in the same journals. Small average differences are still possible, as the sample size was limited. However matching choices have some limitations so results should be interpreted very cautiously. Also, citations by policy sources for re-uses were rare.

TRIAL REGISTRATION

Registration: osf.io/fp62e.

A study protocol of qualitative data sharing practices in clinical trials in the UK and Ireland: towards the production of good practice guidance

Background: Data sharing enables researchers to conduct novel research with previously collected data sets, thus maximising scientific findings and cost effectiveness, and reducing research waste. The value of sharing anonymised data from clinical trials is well recognised with a moderated access approach recommended. While substantial challenges to data sharing remain, there are additional challenges for qualitative data. Qualitative data including videos, interviews, and observations are often more readily identifiable than quantitative data. Existing guidance from UK Economic and Social Research Council applies to sharing qualitative data but does not address the additional challenges related to sharing qualitative data collected within trials, including the need to incorporate the necessary information and consent into already complex recruitment processes, with the additional sensitive nature of health-related data. Methods: Work package 1 will involve separate focus group interviews with members of each stakeholder group: trial managers, clinical trialists, qualitative researchers, members of research funding bodies and trial participants who have been involved in qualitative research. Data will be analysed using thematic analysis and managed within QSR NVivo to enhance transparency. Work package 2 will involve a documentary analysis of current consent procedures for qualitative data collected as part of the conduct of clinical trials. We will include documents such as participant information leaflets and consent forms for the qualitative components in trials. We will extract data such as whether specific clauses for data sharing are included in the consent form. Content analysis will be used to analyse whether and how consent is being obtained for qualitative data sharing. Conclusions: This study will provide insight into the existing practice of sharing of qualitative data in clinical trials and the current issues and opportunities, to help shape future research and development of guidance to encourage maximum learning to be gained from this valuable data.

'It’s not something you can take in your hands'. Swiss experts’ perspectives on health data ownership: an interview-based study

Objectives

The evolution of healthcare and biomedical research into data-rich fields has raised several questions concerning data ownership. In this paper, we aimed to analyse the perspectives of Swiss experts on the topic of health data ownership and control.

Design

In our qualitative study, we selected participants through purposive and snowball sampling. Interviews were recorded, transcribed verbatim and then analysed thematically.

Setting

Semi-structured interviews were conducted in person, via phone or online.

Participants

We interviewed 48 experts (researchers, policy makers and other stakeholders) of the Swiss health-data framework.

Results

We identified different themes linked to data ownership. These include: (1) the data owner: data-subjects versus data-processors; (2) uncertainty about data ownership; (3) labour as a justification for data ownership and (4) the market value of data. Our results suggest that experts from Switzerland are still divided about who should be the data owner and also about what ownership would exactly mean. There is ambivalence between the willingness to acknowledge patients as the data owners and the fact that the effort made by data-processors (eg, researchers) to collect and manage the data entitles them to assert ownership claims towards the data themselves. Altogether, a tendency to speak about data in market terms also emerged.

Conclusions

The development of a satisfactory account of data ownership as a concept to organise the relationship between data-subjects, data-processors and data themselves is an important endeavour for Switzerland and other countries who are developing data governance in the healthcare and research domains. Setting clearer rules on who owns data and on what ownership exactly entails would be important. If this proves unfeasible, the idea that health data cannot truly belong to anyone could be promoted. However, this will not be easy, as data are seen as an asset to control and profit from.

Comparing the use of aggregate data and various methods of integrating individual patient data to network meta-analysis and its application to first-line ART

Background

The 2018 World Health Organization HIV guidelines were based on the results of a network meta-analysis (NMA) of published trials. This study employed individual patient-level data (IPD) and aggregate data (AgD) and meta-regression methods to assess the evidence supporting the WHO recommendations and whether they needed any refinements.

Methods

Access to IPD from three trials was granted through ClinicalStudyDataRequest.com (CSDR). Seven modelling approaches were applied and compared: 1) Unadjusted AgD network meta-analysis (NMA) – the original analysis; 2) AgD-NMA with meta-regression; 3) Two-stage IPD-AgD NMA; 4) Unadjusted one-stage IPD-AgD NMA; 5) One-stage IPD-AgD NMA with meta-regression (one-stage approach); 6) Two-stage IPD-AgD NMA with empirical-priors (empirical-priors approach); 7) Hierarchical meta-regression IPD-AgD NMA (HMR approach). The first two were the models used previously. Models were compared with respect to effect estimates, changes in the effect estimates, coefficient estimates, DIC and model fit, rankings and between-study heterogeneity.

Results

IPD were available for 2160 patients, representing 6.5% of the evidence base and 3 of 24 edges. The aspect of the model affected by the choice of modeling appeared to differ across outcomes. HMR consistently generated larger intervals, often with credible intervals (CrI) containing the null value. Discontinuations due to adverse events and viral suppression at 96 weeks were the only two outcomes for which the unadjusted AgD NMA would not be selected. For the first, the selected model shifted the principal comparison of interest from an odds ratio of 0.28 (95% CrI: 10.17, 0.44) to 0.37 (95% CrI: 0.23, 0.58). Throughout all outcomes, the regression estimates differed substantially between AgD and IPD methods, with the latter being more often larger in magnitude and statistically significant.

Conclusions

Overall, the use of IPD often impacted the coefficient estimates, but not sufficiently as to necessitate altering the final recommendations of the 2018 WHO Guidelines. Future work should examine the features of a network where adjustments will have an impact, such as how much IPD is required in a given size of network.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12874-021-01254-5.

Data Sharing Goals for Nonprofit Funders of Clinical Trials

Sharing clinical trial data can provide value to research participants and communities by accelerating the development of new knowledge and therapies as investigators merge data sets to conduct new analyses, reproduce published findings to raise standards for original research, and learn from the work of others to generate new research questions. Nonprofit funders, including disease advocacy and patient-focused organizations, play a pivotal role in the promotion and implementation of data sharing policies. Funders are uniquely positioned to promote and support a culture of data sharing by serving as trusted liaisons between potential research participants and investigators who wish to access these participants’ networks for clinical trial recruitment. In short, nonprofit funders can drive policies and influence research culture. The purpose of this paper is to detail a set of aspirational goals and forward thinking, collaborative data sharing solutions for nonprofit funders to fold into existing funding policies. The goals of this paper convey the complexity of the opportunities and challenges facing nonprofit funders and the appropriate prioritization of data sharing within their organizations and may serve as a starting point for a data sharing toolkit for nonprofit funders of clinical trials to provide the clarity of mission and mechanisms to enforce the data sharing practices their communities already expect are happening.

Protocol for the development of a repository of individual participant data from randomised controlled trials conducted in adult care homes (the Virtual International Care Homes Trials Archive (VICHTA))

Background

Approximately 418,000 people live in care homes in the UK, yet accessible, robust data on care home populations and organisation are lacking. This hampers our ability to plan, allocate resources or prevent risk. Large randomised controlled trials (RCTs) conducted in care homes offer a potential solution. The value of detailed data on residents’ demographics, outcomes and contextual information captured in RCTs has yet to be fully realised. Irrespective of the intervention tested, much of the trial data collected overlaps in terms of structured assessments and descriptive information. Given the time and costs required to prospectively collect data in these populations, pooling anonymised RCT data into a structured repository offers benefit; secondary analyses of pooled RCT data can improve understanding of this under-researched population and enhance the future trial design. This protocol describes the creation of a project-specific repository of individual participant data (IPD) from trials conducted in care homes and subsequent expansion into a legacy dataset for wider use, to address the need for accurate, high-quality IPD on this vulnerable population.

Methods

Informed by scoping of relevant literature, the principal investigators of RCTs conducted in adult care homes in the UK since 2010 will be invited to contribute trial IPD. Contributing trialists will form a Steering Committee who will oversee data sharing and remain gatekeepers of their own trial’s data. IPD will be cleaned and standardised in consultation with the Steering Committee for accuracy. Planned analyses include a comparison of pooled IPD with point estimates from administrative sources, to assess generalisability of RCT data to the wider care home population. We will also identify key resident characteristics and outcomes from within the trial repository, which will inform the development of a national minimum dataset for care homes. Following project completion, management will migrate to the Virtual Trials Archives, forming a legacy dataset which will be expanded to include international RCTs, and will be accessible to the wider research community for analyses.

Discussion

Analysis of pooled IPD has the potential to inform and direct future practice, research and policy at low cost, enhancing the value of existing data and reducing research waste. We aim to create a permanent archive for care home trial data and welcome the contribution of emerging trial datasets.

Data Sharing in Biomedical Sciences: A Systematic Review of Incentives

Background: The lack of incentives has been described as the rate-limiting step for data sharing. Currently, the evaluation of scientific productivity by academic institutions and funders has been heavily reliant upon the number of publications and citations, raising questions about the adequacy of such mechanisms to reward data generation and sharing. This article provides a systematic review of the current and proposed incentive mechanisms for researchers in biomedical sciences and discusses their strengths and weaknesses. Methods: PubMed, Web of Science, and Google Scholar were queried for original research articles, editorials, and opinion articles on incentives for data sharing. Articles were included if they discussed incentive mechanisms for data sharing, were applicable to biomedical sciences, and were written in English. Results: Although coauthorship in return for the sharing of data is common, this might be incompatible with authorship guidelines and raise concerns over the ability of secondary analysts to contest the proposed research methods or conclusions that are drawn. Data publication, citation, and altmetrics have been proposed as alternative routes to credit data generators, which could address these disadvantages. Their primary downsides are that they are not well-established, it is difficult to acquire evidence to support their implementation, and that they could be gamed or give rise to novel forms of research misconduct. Conclusions: Alternative recognition mechanisms need to be more commonly used to generate evidence on their power to stimulate data sharing, and to assess where they fall short. There is ample discussion in policy documents on alternative crediting systems to work toward Open Science, which indicates that that there is an interest in working out more elaborate metascience programs.

Identification of Common Data Elements from Pivotal FDA Trials

It is difficult to arrive at an efficient and widely acceptable set of common data elements (CDEs). Trial outcomes, as defined in a clinical trial registry, offer a large set of elements to analyze. However, all clinical trial outcomes is an overwhelming amount of information. One way to reduce this amount of data to a usable volume is to only use a subset of trials. Our method uses a subset of trials by considering trials that support drug approval (pivotal trials) by Food and Drug Administration. We identified a set of pivotal trials from FDA drug approval documents and used primary outcomes data for these trials to identify a set of important CDEs. We identified 76 CDEs out of a set of 172 data elements from 192 pivotal trials for 100 drugs. This set of CDEs, grouped by medical condition, can be considered as containing the most significant data elements.

Evaluation of Data Sharing After Implementation of the International Committee of Medical Journal Editors Data Sharing Statement Requirement

IMPORTANCE

The benefits of responsible sharing of individual-participant data (IPD) from clinical studies are well recognized, but stakeholders often disagree on how to align those benefits with privacy risks, costs, and incentives for clinical trialists and sponsors. The International Committee of Medical Journal Editors (ICMJE) required a data sharing statement (DSS) from submissions reporting clinical trials effective July 1, 2018. The required DSSs provide a window into current data sharing rates, practices, and norms among trialists and sponsors.

OBJECTIVE

To evaluate the implementation of the ICMJE DSS requirement in 3 leading medical journals: JAMA, Lancet, and New England Journal of Medicine (NEJM).

DESIGN, SETTING, AND PARTICIPANTS

This is a cross-sectional study of clinical trial reports published as articles in JAMA, Lancet, and NEJM between July 1, 2018, and April 4, 2020. Articles not eligible for DSS, including observational studies and letters or correspondence, were excluded. A MEDLINE/PubMed search identified 487 eligible clinical trials in JAMA (112 trials), Lancet (147 trials), and NEJM (228 trials). Two reviewers evaluated each of the 487 articles independently.

EXPOSURE

Publication of clinical trial reports in an ICMJE medical journal requiring a DSS.

MAIN OUTCOMES AND MEASURES

The primary outcomes of the study were declared data availability and actual data availability in repositories. Other captured outcomes were data type, access, and conditions and reasons for data availability or unavailability. Associations with funding sources were examined.

RESULTS

A total of 334 of 487 articles (68.6%; 95% CI, 64%-73%) declared data sharing, with nonindustry NIH-funded trials exhibiting the highest rates of declared data sharing (89%; 95% CI, 80%-98%) and industry-funded trials the lowest (61%; 95% CI, 54%-68%). However, only 2 IPD sets (0.6%; 95% CI, 0.0%-1.5%) were actually deidentified and publicly available as of April 10, 2020. The remaining were supposedly accessible via request to authors (143 of 334 articles [42.8%]), repository (89 of 334 articles [26.6%]), and company (78 of 334 articles [23.4%]). Among the 89 articles declaring that IPD would be stored in repositories, only 17 (19.1%) deposited data, mostly because of embargo and regulatory approval. Embargo was set in 47.3% of data-sharing articles (158 of 334), and in half of them the period exceeded 1 year or was unspecified.

CONCLUSIONS AND RELEVANCE

Most trials published in JAMA, Lancet, and NEJM after the implementation of the ICMJE policy declared their intent to make clinical data available. However, a wide gap between declared and actual data sharing exists. To improve transparency and data reuse, journals should promote the use of unique pointers to data set location and standardized choices for embargo periods and access requirements.

Transparency of Regulatory Data across the European Medicines Agency, Health Canada, and US Food and Drug Administration

Based on an analysis of relevant laws and policies, regulator data portals, and information requests, we find that clinical data, including clinical study reports, submitted to the European Medicines Agency and Health Canada to support approval of medicines are routinely made publicly available.

Decrease in Salivary Serotonin in Response to Probiotic Supplementation With Saccharomyces boulardii in Healthy Volunteers Under Psychological Stress: Secondary Analysis of a Randomized, Double-Blind, Placebo-Controlled Trial

BACKGROUND

Bacterial probiotics are thought to exert a serotonergic effect relevant to their potential antidepressant and pro-cognitive action, but yeast probiotics have not been tested. The aim of the present study was to determine whether 30-day supplementation with Saccharomyces boulardii affects the level of salivary serotonin under psychological stress and identify the factors associated with it.

METHODS

Healthy medical students were randomized to ingest Saccharomyces boulardii CNCM I-1079 or placebo before a stressful event. Salivary serotonin concentration was assessed before and at the end of supplementation. Moreover, obtained results were compared to psychological, biochemical, physiological and sociodemographic study participants data.

RESULTS

Data of thirty-two participants (22.8 ± 1.7 years of age, 16 males) was available for the main analysis. Supplementation with Saccharomyces boulardii decreased salivary serotonin concentration under psychological stress by 3.13 (95% CI 0.20 to 6.07) ng/mL, p = 0.037, as compared to placebo. Salivary serotonin was positively correlated with salivary metanephrine (β = 0.27, 95% CI 0.02 to 0.52, p = 0.031) and pulse rate (β = 0.28, 95% CI 0.05 to 0.50, p = 0.018), but insignificantly with anxiety, depression, eating attitudes and information retrieval.

CONCLUSIONS

Saccharomyces boulardii CNCM I-1079 may be distinct from bacterial probiotics in its salivary serotonergic effect, which appears positively linked to symapathoadrenal markers. The study requires cautious interpretation, and further investigation.

Reducing bias and improving transparency in medical research: a critical overview of the problems, progress and suggested next steps

In recent years there has been increasing awareness of problems that have undermined trust in medical research. This review outlines some of the most important issues including research culture, reporting biases, and statistical and methodological issues. It examines measures that have been instituted to address these problems and explores the success and limitations of these measures. The paper concludes by proposing three achievable actions which could be implemented to deliver significantly improved transparency and mitigation of bias. These measures are as follows: (1) mandatory registration of interests by those involved in research; (2) that journals support the ‘registered reports’ publication format; and (3) that comprehensive study documentation for all publicly funded research be made available on a World Health Organization research repository. We suggest that achieving such measures requires a broad-based campaign which mobilises public opinion. We invite readers to feedback on the proposed actions and to join us in calling for their implementation.

Transparency in surgical randomized clinical trials: cross-sectional observational study

BACKGROUND

RCTs provide the scientific basis upon which treatment decisions are made. To facilitate critical review, it is important that methods and results are reported transparently. The aim of this study was to explore transparency in surgical RCTs with respect to trial registration, disclosure of funding sources, declarations of investigator conflicts and data-sharing.

METHODS

This was a cross-sectional review of published surgical RCTs. Ten high-impact journals were searched systematically for RCTs published in years 2009, 2012, 2015 and 2018. Four domains of transparency were explored: trial registration, disclosure of funding, disclosure of investigator conflicts, and a statement relating to data-sharing.

RESULTS

Of 611 RCTs, 475 were eligible for analysis. Some 397 RCTs (83.6 per cent) were registered on a trial database, of which 190 (47·9 per cent) had been registered prospectively. Prospective registration increased over time (26 per cent in 2009, 33·0 per cent in 2012, 54 per cent in 2015, and 72·7 per cent in 2018). Funding disclosure was present in 55·0, 65·0, 69·4 and 75·4 per cent of manuscripts respectively. Conflict of interest disclosure was present in 49·5, 89·1, 94·6 and 98·3 per cent of manuscripts across the same time periods. Data-sharing statements were present in only 15 RCTs (3·2 per cent), 11 of which were published in 2018.

CONCLUSION

Trial registration, disclosure of funding and disclosure of investigator conflicts in surgical RCTs have improved markedly over the past 10 years. Disclosure of data-sharing plans is exceptionally low. This may contribute to research waste and represents a target for improvement.

From Return of Information to Return of Value: Ethical Considerations when Sharing Individual-Level Research Data

The implementation of digital health technologies into research studies for Alzheimer's disease and other clinical populations is on the rise. Digital tools and strategies create opportunities to further expand the framework for conducting research beyond the traditional medical research model. The combination of participatory and community-based research methods, electronic health records, and the creation of multi-dimensional, large-scale research platforms to support precision medicine, along with the Internet of Things era, have led to more engaged and informed research participants. Research participants increasingly possess an expectation they will play a critical role as partners in the design and conduct of research. Moreover, there is growing interest among research participants to have access to individual-level research data in real-time and/or at study completion. The traditional medical research model is largely one-directional where participants contribute data that is analyzed by researchers to yield generalizable knowledge. In this Ethics Review, we discuss a framework for a more nuanced intermediate research model, which is largely bidirectional and individually customized. Based on the seven ethical guidelines adopted by the National Institutes of Health, we speak to the ethical challenges of this intermediate type research. We also introduce a concept we are calling "MyTerms," in which prospective participants tailor the terms and conditions of informed consent to their personalized preferences for receiving information, including research results. Digital health technologies offer a convenient and flexible approach for researchers to develop protocols that make it possible for participants to obtain access to their study data in a personalized and meaningful way.