Background Sharing of participant-level clinical trial data has potential benefits, but concerns about potential harms to research participants have led some pharmaceutical sponsors and investigators to urge caution. Little is known about clinical trial participants' perceptions of the risks of data sharing. Methods We conducted a structured survey of 771 current and recent participants from a diverse sample of clinical trials at three academic medical centers in the United States. Surveys were distributed by mail (350 completed surveys) and in clinic waiting rooms (421 completed surveys) (overall response rate, 79%). Results Less than 8% of respondents felt that the potential negative consequences of data sharing outweighed the benefits. A total of 93% were very or somewhat likely to allow their own data to be shared with university scientists, and 82% were very or somewhat likely to share with scientists in for-profit companies. Willingness to share data did not vary appreciably with the purpose for which the data would be used, with the exception that fewer participants were willing to share their data for use in litigation. The respondents' greatest concerns were that data sharing might make others less willing to enroll in clinical trials (37% very or somewhat concerned), that data would be used for marketing purposes (34%), or that data could be stolen (30%). Less concern was expressed about discrimination (22%) and exploitation of data for profit (20%). Conclusions In our study, few clinical trial participants had strong concerns about the risks of data sharing. Provided that adequate security safeguards were in place, most participants were willing to share their data for a wide range of uses. (Funded by the Greenwall Foundation.).

/pdf/clinical-trial-participants-views-of-the-risks-and-benefits-jljxxshgmr.pdf

Clinical Trial Participants' Views of the Risks and Benefits of Data Sharing

Data sharing

Over the past decade, artificial intelligence (AI) has contributed substantially to the resolution of various medical problems, including cancer. Deep learning (DL), a subfield of AI, is characterized by its ability to perform automated feature extraction and has great power in the assimilation and evaluation of large amounts of complicated data. On the basis of a large quantity of medical data and novel computational technologies, AI, especially DL, has been applied in various aspects of oncology research and has the potential to enhance cancer diagnosis and treatment. These applications range from early cancer detection, diagnosis, classification and grading, molecular characterization of tumors, prediction of patient outcomes and treatment responses, personalized treatment, automatic radiotherapy workflows, novel anti-cancer drug discovery, and clinical trials. In this review, we introduced the general principle of AI, summarized major areas of its application for cancer diagnosis and treatment, and discussed its future directions and remaining challenges. As the adoption of AI in clinical use is increasing, we anticipate the arrival of AI-powered cancer care.

https://onlinelibrary.wiley.com/doi/pdf/10.1002/cac2.12215

Artificial intelligence for assisting cancer diagnosis and treatment in the era of precision medicine

Recent evidence suggests that there is often substantial variation in the benefits and harms across a trial population. We aimed to identify regression modeling approaches that assess heterogeneity of treatment effect within a randomized clinical trial. We performed a literature review using a broad search strategy, complemented by suggestions of a technical expert panel. The approaches are classified into 3 categories: 1) Risk-based methods (11 papers) use only prognostic factors to define patient subgroups, relying on the mathematical dependency of the absolute risk difference on baseline risk; 2) Treatment effect modeling methods (9 papers) use both prognostic factors and treatment effect modifiers to explore characteristics that interact with the effects of therapy on a relative scale. These methods couple data-driven subgroup identification with approaches to prevent overfitting, such as penalization or use of separate data sets for subgroup identification and effect estimation. 3) Optimal treatment regime methods (12 papers) focus primarily on treatment effect modifiers to classify the trial population into those who benefit from treatment and those who do not. Finally, we also identified papers which describe model evaluation methods (4 papers). Three classes of approaches were identified to assess heterogeneity of treatment effect. Methodological research, including both simulations and empirical evaluations, is required to compare the available methods in different settings and to derive well-informed guidance for their application in RCT analysis.

/pdf/predictive-approaches-to-heterogeneous-treatment-effects-a-1jnl6n42nm.pdf

Predictive approaches to heterogeneous treatment effects: a scoping review.

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, andNew England Journal of Medicine(NEJM). Design, Setting, and Participants This is a cross-sectional study of clinical trial reports published as articles inJAMA,Lancet, andNEJMbetween 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 inJAMA(112 trials),Lancet(147 trials), andNEJM(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 inJAMA,Lancet, andNEJMafter 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.

/pdf/evaluation-of-data-sharing-after-implementation-of-the-4qj5wc5tb0.pdf

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

Access to investigational drugs for patients with amyotrophic lateral sclerosis in the USA

The Johnson & Johnson vision for data sharing We believe in responsible sharing of clinical trial data that advances science and respects individual participants, who give their time and even take risks to contribute to medicine. In keeping with that belief, our company, Johnson & Johnson (NJ, USA), has established a data sharing agreement with the Yale Open Data Access Project, an independent academic institution, to review and decide, based on scientific merit, whether requests for access to our clinical trial data, for both pharmaceutical and medical device products, will be fulfilled [1]. Sharing clinical trial data has the potential to improve public health by providing a better understanding of physiology and pathophysiology, as well as both the benefits and risks of all types of treatments, including medicines and devices. Importantly, it also enables a powerful approach to address important questions that could not be addressed within individual studies by using either a combined analysis or metaanalysis, enabling a far deeper understanding of diseases, subgroups of patients who might respond differently and strengthening the evidence base for future studies, treatment guidelines, r egulatory, payer and medical decisions [2]. Recently, the Institute of Medicine released a comprehensive report, which offers recommendations for responsible data sharing and a vision of an ecosystem in which clinical trial data from all sources are more broadly accessible to the research community [3]. We support the Institute of Medicine’s recommendations and agree on the need to move forward, through multistakeholder groups, some of which are already working on various aspects of data transparency and approaches to harmonization, including the multiregional clinical trial forum [4] and TransCelerate [5]. We agree that the greatest value from data sharing efforts will only be realized if data sharing is agreed and embraced broadly. This will require all stakeholders who conduct clinical trials of all types and of all interventions to participate in data sharing, as well as an effort towards more common data collection tools and standards. Of course, all efforts have to be aimed at maintaining the highest possible scientific principles.

The need for even further progress with clinical trial data sharing efforts: patients are waiting

Background Sharing data from clinical trials could assist with the advancement of science and medicine, potentially providing a better understanding of both the benefits and risks of medicines and other treatments. Sharing data also allows for questions to be addressed at the meta-analysis level that cannot be addressed within individual studies. Purpose In this article, we offer some practical recommendations that will allow researchers to readily combine datasets from different studies and sources, thereby enabling meta-analyses that could have significant impact on advancing medicine. Methods The authors relied on their collective experience in the conduct and reporting of clinical trials to define the areas of potential concern related to responsible sharing of clinical trial data. We conducted a review of the literature and engaged in an iterative consensus-building process. Results To further the goal of responsible sharing of clinical trial data, collaboration on a consistent set of data standards and methods across both industry and academia is sorely needed. Protection of participant privacy is a paramount principle. The additional questions of who maintains, funds, and oversees databases of participantlevel data will be important to resolve. Requiring researchers to register their requests for participant-level data and to provide details of their intended research would allow others to evaluate the proposed research plan, consistent with the principles of science and transparency. Limitations The recommendations represent the views of the individual authors. We recognize that other approaches to data sharing that have been advocated are also based on sound ethical and scientific principles. Clinical Trials 2014; 11: 7–12. http://ctj.sagepub.com

/pdf/bumps-and-bridges-on-the-road-to-responsible-sharing-of-2ev9onq40a.pdf

Bumps and bridges on the road to responsible sharing of clinical trial data

The Yale University Open Data Access (YODA) Project has facilitated access to clinical trial data since 2013. The purpose of this article is to provide an overview of the Project, describe key decisions that were made when establishing data sharing policies, and suggest how our experience and the experiences of our first two data generator partners, Medtronic, Inc. and Johnson & Johnson, can be used to enhance other ongoing or future initiatives.

/pdf/overview-and-experience-of-the-yoda-project-with-clinical-1z48bvdlvn.pdf

Overview and experience of the YODA Project with clinical trial data sharing after 5 years.

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Access to investigational drugs for patients with amyotrophic lateral sclerosis in the USA

The need for even further progress with clinical trial data sharing efforts: patients are waiting

Bumps and bridges on the road to responsible sharing of clinical trial data

Overview and experience of the YODA Project with clinical trial data sharing after 5 years.