Archives des mots-clés : biomedical research

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

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Authors : Valentin Danchev, Yan Min, John Borghi, Mike Baiocchi, John P. A. Ioann

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.

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

DOI :10.1001/jamanetworkopen.2020.33972

Methodological quality of COVID-19 clinical research

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Authors : Richard G. Jung, Pietro Di Santo, Cole Clifford, Graeme Prosperi-Porta, Stephanie Skanes, Annie Hung, Simon Parlow, Sarah Visintini, F. Daniel Ramirez, Trevor Simard & Benjamin Hibbert

The COVID-19 pandemic began in early 2020 with major health consequences. While a need to disseminate information to the medical community and general public was paramount, concerns have been raised regarding the scientific rigor in published reports.

We performed a systematic review to evaluate the methodological quality of currently available COVID-19 studies compared to historical controls. A total of 9895 titles and abstracts were screened and 686 COVID-19 articles were included in the final analysis.

Comparative analysis of COVID-19 to historical articles reveals a shorter time to acceptance (13.0[IQR, 5.0–25.0] days vs. 110.0[IQR, 71.0–156.0] days in COVID-19 and control articles, respectively; p < 0.0001).

Furthermore, methodological quality scores are lower in COVID-19 articles across all study designs. COVID-19 clinical studies have a shorter time to publication and have lower methodological quality scores than control studies in the same journal. These studies should be revisited with the emergence of stronger evidence.

URL : Methodological quality of COVID-19 clinical research

DOI : https://doi.org/10.1038/s41467-021-21220-5

COVID‐19 and the generation of novel scientific knowledge: Evidence‐based decisions and data sharing

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Authors : Lucie Perillat, Brian S. Baigrie

Rationale, aims and objectives

The COVID‐19 pandemic has impacted every facet of society, including medical research. This paper is the second part of a series of articles that explore the intricate relationship between the different challenges that have hindered biomedical research and the generation of novel scientific knowledge during the COVID‐19 pandemic.

In the first part of this series, we demonstrated that, in the context of COVID‐19, the scientific community has been faced with numerous challenges with respect to (1) finding and prioritizing relevant research questions and (2) choosing study designs that are appropriate for a time of emergency.

Methods

During the early stages of the pandemic, research conducted on hydroxychloroquine (HCQ) sparked several heated debates with respect to the scientific methods used and the quality of knowledge generated.

Research on HCQ is used as a case study in both papers. The authors explored biomedical databases, peer‐reviewed journals, pre‐print servers and media articles to identify relevant literature on HCQ and COVID‐19, and examined philosophical perspectives on medical research in the context of this pandemic and previous global health challenges.

Results

This second paper demonstrates that a lack of research prioritization and methodological rigour resulted in the generation of fleeting and inconsistent evidence that complicated the development of public health guidelines.

The reporting of scientific findings to the scientific community and general public highlighted the difficulty of finding a balance between accuracy and speed.

Conclusions

The COVID‐19 pandemic presented challenges in terms of (3) evaluating evidence for the purpose of making evidence‐based decisions and (4) sharing scientific findings with the rest of the scientific community.

This second paper demonstrates that the four challenges outlined in the first and second papers have often compounded each other and have contributed to slowing down the creation of novel scientific knowledge during the COVID‐19 pandemic.

DOI : https://doi.org/10.1111/jep.13548

‘Nepotistic journals’: a survey of biomedical journals

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Authors : Alexandre Scanff, Florian Naudet, Ioana Cristea, David Moher, Dorothy V M Bishop, Clara Locher

Context

Convergent analyses in different disciplines support the use of the Percentage of Papers by the Most Prolific author (PPMP) as a red flag to identify journals that can be suspected of questionable editorial practices. We examined whether this index, complemented by the Gini index, could be useful for identifying cases of potential editorial bias, using a large sample of biomedical journals.

Methods

We extracted metadata for all biomedical journals referenced in the National Library of Medicine, with any attributed Broad Subject Terms, and at least 50 authored (i.e. by at least one author) articles between 2015 and 2019, identifying the most prolific author (i.e. the person who signed the most papers in each particular journal).

We calculated the PPMP and the 2015-2019 Gini index for the distribution of articles across authors. When the relevant information was reported, we also computed the median publication lag (time between submission and acceptance) for articles authored by any of the most prolific authors and that for articles not authored by prolific authors.

For outlier journals, defined as a PPMP or Gini index above the 95th percentile of their respective distributions, a random sample of 100 journals was selected and described in relation to status on the editorial board for the most prolific author.

Results

5 468 journals that published 4 986 335 papers between 2015 and 2019 were analysed. The PPMP 95th percentile was 10.6% (median 2.9%). The Gini index 95th percentile was 0.355 (median 0.183). Correlation between the two indices was 0.35 (95CI 0.33 to 0.37). Information on publication lag was available for 2 743 journals.

We found that 277 journals (10.2%) had a median time lag to publication for articles by the most prolific author(s) that was shorter than 3 weeks, versus 51 (1.9%) journals with articles not authored by prolific author(s).

Among the random sample of outlier journals, 98 provided information about their editorial board. Among these 98, the most prolific author was part of the editorial board in 60 cases (61%), among whom 25 (26% of the 98) were editors-in-chief.

Discussion

In most journals publications are distributed across a large number of authors. Our results reveal a subset of journals where a few authors, often members of the editorial board, were responsible for a disproportionate number of publications.

The papers by these authors were more likely to be accepted for publication within 3 weeks of their submission. To enhance trust in their practices, journals need to be transparent about their editorial and peer review practices.

URL : ‘Nepotistic journals’: a survey of biomedical journals

DOI : https://doi.org/10.1101/2021.02.03.429520

Influence and management of conflicts of interest in randomised clinical trials: qualitative interview study

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Authors : Lasse Østengaard, Andreas Lundh, Tine Tjørnhøj-Thomsen, Suhayb Abdi, Mustafe H A Gelle, Lesley A Stewart, Isabelle Boutron, Asbjørn Hróbjartsson

Objective

To characterise and analyse the experiences of trial researchers of if and how conflicts of interest had unduly influenced clinical trials they had worked on, what management strategies they had used to minimise any potential influence, and their experiences and views on conflicts of interest more generally.

Design

Qualitative interview study.

Participants

Trial researchers who had participated in at least 10 clinical trials with methodological or statistical expertise. Researchers differed by geographical location, educational background, and experience with different types of funders. Interviewees were identified by searches on Web of Science and snowball sampling. 52 trial researchers were approached by email; 20 agreed to be interviewed.

Setting

Interviews conducted by telephone, recorded, transcribed verbatim, imported to NVivo 12, and analysed by systematic text condensation. Semistructured interviews focused on financial and non-financial conflicts of interest.

Results

The interviewees had participated in a median of 37.5 trials and were mainly male physicians who had experience with commercial and non-commercial trial funders. Two predefined themes (influence of conflicts of interest and management strategies) and two additional themes (definition and reporting of conflicts of interest) emerged.

Examples of perceived influence of conflicts of interest were: choice of inferior comparator, manipulation of the randomisation process, prematurely stopping the trials, fabrication of data, blocking access to data, and spin (eg, overly favourable interpretation of the results).

Examples of strategies to manage conflicts of interest were: disclosure procedures, exclusion of the funder from design and analysis, independent committees, contracts ensuring complete access to the data, and no restriction by the funder on analysis and reporting.

Interviewees used different definitions or thresholds for what they considered to be conflicts of interest, and they described different criteria for when to report them. Some interviewees considered non-commercial financial conflicts of interest (eg, funding of trials by governmental health agencies with a political agenda) to be equally or more important than commercial financial conflicts of interest (eg, funding by drug and device companies), but more challenging to report and manage.

Conclusion

This study described how trial researchers perceive conflicts of interest unduly influencing clinical trials they had worked on, and the management strategies they used to prevent these influences.

The results indicated considerable variability in researchers’ understanding of what conflicts of interest are and when they should be reported.

URL : Influence and management of conflicts of interest in randomised clinical trials: qualitative interview study

DOI : https://doi.org/10.1136/bmj.m3764

The Rigor and Transparency Index Quality Metric for Assessing Biological and Medical Science Methods

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Authors : Joe Menke, Martijn Roelandse, Burak Ozyurt, Maryann Martone, Anita Bandrowski

The reproducibility crisis is a multifaceted problem involving ingrained practices within the scientific community. Fortunately, some causes are addressed by the author’s adherence to rigor and reproducibility criteria, implemented via checklists at various journals.

We developed an automated tool (SciScore) that evaluates research articles based on their adherence to key rigor criteria, including NIH criteria and RRIDs, at an unprecedented scale. We show that despite steady improvements, less than half of the scoring criteria, such as blinding or power analysis, are routinely addressed by authors; digging deeper, we examined the influence of specific checklists on average scores.

The average score for a journal in a given year was named the Rigor and Transparency Index (RTI), a new journal quality metric. We compared the RTI with the Journal Impact Factor and found there was no correlation. The RTI can potentially serve as a proxy for methodological quality.

URL : The Rigor and Transparency Index Quality Metric for Assessing Biological and Medical Science Methods

DOI : https://doi.org/10.1016/j.isci.2020.101698

Deep Learning in Mining Biological Data

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Authors : Mufti Mahmud, M. Shamim Kaiser, T. Martin McGinnity, Amir Hussain

Recent technological advancements in data acquisition tools allowed life scientists to acquire multimodal data from different biological application domains. Categorized in three broad types (i.e. images, signals, and sequences), these data are huge in amount and complex in nature.

Mining such enormous amount of data for pattern recognition is a big challenge and requires sophisticated data-intensive machine learning techniques. Artificial neural network-based learning systems are well known for their pattern recognition capabilities, and lately their deep architectures—known as deep learning (DL)—have been successfully applied to solve many complex pattern recognition problems.

To investigate how DL—especially its different architectures—has contributed and been utilized in the mining of biological data pertaining to those three types, a meta-analysis has been performed and the resulting resources have been critically analysed. Focusing on the use of DL to analyse patterns in data from diverse biological domains, this work investigates different DL architectures’ applications to these data.

This is followed by an exploration of available open access data sources pertaining to the three data types along with popular open-source DL tools applicable to these data. Also, comparative investigations of these tools from qualitative, quantitative, and benchmarking perspectives are provided.

Finally, some open research challenges in using DL to mine biological data are outlined and a number of possible future perspectives are put forward.

URL : Deep Learning in Mining Biological Data

DOI : https://doi.org/10.1007/s12559-020-09773-x