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IOM Attempting to Set Principles for Responsible Clinical Trial Data Sharing

2014· article· en· W2326706375 on OpenAlexaboutno aff
Peggy Eastman

Bibliographic record

VenueOncology Times · 2014
Typearticle
Languageen
FieldMedicine
TopicEthics in Clinical Research
Canadian institutionsnot available
Fundersnot available
KeywordsData sharingSet (abstract data type)Data setClinical trialComputer scienceMedicineAlternative medicineArtificial intelligenceInternal medicine

Abstract

fetched live from OpenAlex

FigureWASHINGTON—Sharing data from clinical trials holds the promise of broadening the value of such data and putting therapies used in clinical practice on a firmer foundation in oncology and other fields. But, because such data-sharing has to be done in a careful and responsible way, a committee of the Institute of Medicine (IOM) has issued a new preliminary report setting forth a “framework” of guiding principles for the sharing of clinical trial data. The group is now also seeking suggestions from the public (see next page), with a final report expected in mid-2015. “Ultimately, such data sharing—if carried out appropriately—could lead to improved clinical care and greater public trust in clinical research and health care,” Committee Chair Bernard Lo, MD, said in his preface to the report. “However, recommending strategies for how clinical trial data could be shared responsibly is a difficult process.” Lo is President and CEO of the Greenwall Foundation, which funds bioethical research, and former Professor of Medicine and Director of the Program in Medical Ethics at the University of California, San Francisco. Also among the committee members is Jeffrey Drazen, MD, Editor-in-Chief of the New England Journal of Medicine and a senior physician at Brigham and Women's Hospital. The published preliminary report, “Discussion Framework for Clinical Trial Data Sharing: Guiding Principles, Elements, and Activities,” is part of an ongoing IOM study on how data from clinical trials (both summary data and individual patient data) might best be shared. Fifteen years ago, sharing data in the competitive world of biomedical science might have been viewed with skepticism. In the time since, however, the concept of sharing clinical trial data has evolved and matured over the last 10 years. “The data-sharing movement has gained substantial momentum during the last decade, in both the clinical trial and larger scientific communities. A cultural change has occurred in which the conversation around data sharing has moved from whether it should happen to how it can be carried out,” the report notes. Today, the Pharmaceutical Research and Manufacturers of America (PhRMA), whose members spend millions to develop new therapies, supports responsible data sharing, as do many other research and health care organizations. Last summer, the American Society of Clinical Oncology joined more than 70 leading health care, research, and advocacy organizations in an international alliance of more than 40 countries whose goal is to enable responsible sharing of genomic and clinical data. “Collaboration has been integral to our success in the field of oncology,” ASCO President Clifford A. Hudis, MD, said in a statement at that time. “ASCO's participation in this global alliance underscores our belief that standards for responsible and ethical data sharing are crucial for the fastest possible development of better treatments for our patients.” The American Association for Cancer Research is also part of the new alliance. “Cancer is one of the earliest and most compelling areas of medicine in which genomics is providing benefit to patients,” said AACR President Charles Sawyers, MD, Chair of the Program in Human Oncology and Pathogenesis at Memorial Sloan-Kettering Cancer Center. “Our ability to provide accurate information to patients, and to learn why some people respond to therapy and others do not, requires the international approach and shared standards that the alliance aims to create.” Other members are Cancer Research UK, the Canadian Cancer Society, the U.S. National Cancer Institute, the U.K. Wellcome Trust, the U.S. National Institutes of Health, the American Society of Human Genetics, and many academic institutions worldwide, including the University of Cape Town in South Africa. Each organization in the alliance has signed a non-binding letter of intent, pledging to work together to create a not-for-profit public/private non-governmental organization to develop a common international framework for secure sharing of genomic and clinical data. Guiding Principles The IOM report includes the following guiding principles for responsible clinical trial data-sharing: Respect the individual participants whose data are shared; Maximize the benefits to participants in clinical trials and to society, while minimizing harm; Increase public trust in clinical trials; and Carry out sharing of clinical trial data in a manner that enhances fairness. On the first guiding principle, the report notes that the rights, privacy, and welfare of study subjects must always be paramount. If the shared data are de-identified, current U.S. federal regulations on human research protections and privacy do allow other researchers to use the data under certain conditions without consent from the original clinical trial participants (who gave their informed consent initially). But de-identification methods must be rigorous and trustworthy. On the second principle, the report notes that data sharing allows clinical trial subjects to broaden their contribution to society. Data-sharing benefits can include: reduced duplication of the effort and costs of future trials; reduced exposure of future trial participants to avoidable harms identified through data sharing; a deeper knowledge base for regulatory decisions; a broader base for the development of clinical practice guidelines; and a broader base for clinical decision-making. However, the report notes, “In a specific trial, the benefits and risks of the study arms are not known at the outset,” and in some cases the trial's intervention arm could have a worse outcome than the control arm. Therefore, trial subjects should have as clear an understanding as possible of the risks as well as the benefits of their participation so that shared data is grounded in truly informed consent on the part of altruistic study subjects. On the third point, the report notes that “by increasing the transparency of trial design and the pathway to trial conclusions, sharing clinical trial data might increase public trust in the outcomes of specific trials and of clinical trials generally.” Thus, data sharing might hold the promise of boosting the very low rate of adult clinical trial participation. Regarding the fourth point, the report notes that it is very important to be sensitive to applying principles of fairness to all trial participants, but especially to those considered the most vulnerable: “Data sharing should include protections for participant subgroups that are particularly vulnerable to breaches of confidentiality or other consequences of data sharing.” For example, whole genome sequencing data could be identifiable, which might be considered a red flag putting certain trial participants at heightened risk; for this reason, additional safeguards for genomic data-sharing might be needed. Key Topics for Clinical Trial Data Sharing The IOM committee notes that it welcomes public comments–at http://bit.ly/1hPGjmW—on the following topics: Global implementation and practical considerations: How can clinical trial data be shared in the global context? How can different national regulations for research participants be taken into account? How might strategies regarding data-sharing take into account trials conducted in resource-poor settings? Timing and prioritization: How might different types of clinical trial data and uses of shared data be given priority for data sharing? What might be the advantages and disadvantages to various stakeholders of sharing different types of datasets at different points in time after the completion of a clinical trial? Should data sharing be applied only to new trials, or retroactively applied? Mitigating risks: What could be done to minimize the risks to patients and to public health from the dissemination of findings from invalid analyses of shared clinical trial data? Are current de-identification methodologies to protect clinical trial participants sufficient? Under what circumstances might it be beneficial to re-identify trial subjects (to warn them of a safety problem, for example)? Enhancing incentives: What incentives and protections might be established to encourage clinical trial sponsors and investigators to share their data without unduly restricting them? What responsibility should the primary investigator and research institution take to support secondary users in their interpretation of shared data? Measuring impact: What would be appropriate outcome measures to assess the usefulness of different models of clinical trial data sharing? How can these measures be used to guide improvement in data sharing practices?

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How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.044
metaresearch head score (Gemma)0.249
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.640
Threshold uncertainty score0.985

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0440.249
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.874
GPT teacher head0.705
Teacher spread0.169 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2014
Admission routes1
Has abstractyes

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