Developing a data sharing community for spinal cord injury research

ObjectiveTo determine whether and to what degree bias and underestimated variability undermine the predictive value of preclinical research for clinical translation. MethodsWe investigated experimental spinal cord injury (SCI) studies for outcome heterogeneity and the impact of bias. Data from 549 preclinical SCI studies including 9,535 animals were analyzed with meta-regression to assess the effect of various study characteristics and the quality of neurologic recovery. ResultsOverall, the included interventions reported a neurobehavioral outcome improvement of 26.3% (95% confidence interval 24.3-28.4). Response to treatment was dependent on experimental modeling paradigms (neurobehavioral score, site of injury, and animal species). Applying multiple outcome measures was consistently associated with smaller effect sizes compared with studies applying only 1 outcome measure. More than half of the studies (51.2%) did not report blinded assessment, constituting a likely source of evaluation bias, with an overstated effect size of 7.2%. Assessment of publication bias, which extrapolates to identify likely missing data, suggested that between 2% and 41% of experiments remain unpublished. Inclusion of these theoretical missing studies suggested an overestimation of efficacy, reducing the effect sizes by between 0.9% and 14.3%. ConclusionsWe provide empirical evidence of prevalent bias in the design and reporting of experimental SCI studies, resulting in overestimation of the effectiveness. Bias compromises the internal validity and jeopardizes the successful translation of SCI therapies from the bench to bedside. Glossary BBB = Basso, Beattie, Bresnahan; BMS = Basso Mouse Scale for Locomotion; CAMARADES = Collaborative Approach to Meta-Analysis and Review of Animal Data From Experimental Studies; CI = confidence interval; OEG = olfactory ensheathing glia; SCI = spinal cord injury.To allow a more homogeneous group characterized by similar effect sizes (ESs), a rodent analysis was completed identifying the Basso, Beattie, and Bresnahan (BBB)/Basso Mouse Scale (BMS) only group as comparable with regard to the main outcome parameter (improved locomotor function, figure 1B, red square). Funnel plots illustrating the precision (1 divided by the SEM, y-axis) plotted against the standardized ES (x-axis). Each black dot represents 1 experiment. In the absence of publication bias, the points should resemble an inverted funnel. Black dotted line indicates the reported ES before inclusion of missing studies; red line, after trim and till analysis. (A) Decompression: reported ES 27.1% (95% confidence interval [CI] 15.1-39.1, n = 22 studies), adjusted ESs 12.8% (95% CI 1.7-24.0, n = 31). (B) Exercise: reported ES 14.4% (95% CI 6.4-22.3, n = 35), adjusted ES 1.9% (95% CI −6.9 to 10.7, n = 42). (C) Hypothermia: reported ES 16.3% (95% CI 10.3-22.3) without adjustments. (D) Olfactory ensheathing glia (OEG): reported ES 20.1% (95% CI 15.2-24.9, n = 53), adjusted ES 19.2 (95% CI 14.4-24.0, n = 54). (E) Rho/ROCK inhibitors: reported ES 21...

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“…4. Develop data-sharing models [49][50][51] and international cooperation in preclinical SCI research to learn more about underlying differences.…”

Section: Unstable (Transient) Treatment Effectsmentioning

confidence: 99%

Outcome heterogeneity and bias in acute experimental spinal cord injury

et al. 2019

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“…Even in frontrunner areas such as genomics [75] and especially for data from research other than clinical trials [76], a lot of changes are still needed for that (r)evolution to be complete-both in the publishing of data and the using of published data. As argued by Callahan et al [71]. and others, this includes consensus of research disciplines around terms and definitions (ontologies, CDEs), further development of data stewardship policies and data sharing infrastructure (including data security and quality control on data creation, description, and use), protection of intellectual property and appropriate citation of data, and user-friendly interfaces for data upload and download [71,77].…”

Section: Resultsmentioning

confidence: 99%

“…As of this writing, there are no repositories specialized in SCI data, whether preclinical, clinical or community functioning-focused [70]. Some preliminary steps that will make exchange of truly useful data sets feasible have been completed, though [71]. However, the current unavailability of a SCI-focused repository should not stop SCI researchers from sharing their datasets as open data; options 1, 2, and 6 above are open to them.…”

Section: An Interesting Option Is Offered By the Us Federalmentioning

confidence: 99%

A beginner’s guide to data stewardship and data sharing

Dijkers

2019

Spinal Cord

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Study design A narrative review of principles, benefits and disadvantages, as well as methods of research data sharing. Objectives To assist prospective Spinal Cord authors and others with understanding and implementing data sharing, so that various benefits of such sharing can accrue to all spinal cord injury research stakeholders. Setting International. Methods The medical research and health care services literature was reviewed nonsystematically for relevant articles, and web sites were explored for information and services offered by various pertinent organizations. Results Grant makers, professional organizations, research journals, publishers, and other entities in the research field increasingly stress the ethics as well as societal and practical benefits of data sharing, and require researchers to do so within a reasonable time after data collection ends. Sharing data, retrospectively, generally requires much time and resources, but when a data management plan is part of a research proposal from the start, costs are limited, and grant makers allow these costs to be part of a budget. There are many organizations that offer information on or even assist with preparing data for sharing and actual deposit in a data repository. Conclusions The requirement of data sharing is not likely to go away, and researchers interested in submitting their reports to Spinal Cord would do well to familiarize themselves with the myriad practical issues involved in preparing data for sharing.

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“…IMPACT demonstrated the way "deepening" long-tail data can provide incredible insights and even revolutionize treatment. Another example is the recently established data sharing community for spinal cord injury (SCI) research [14].…”

Section: The Long Tail Datamentioning

confidence: 99%

Data Models in Neuroinformatics

Tsur¹

2018

Bioinformatics in the Era of Post Genomics and Big Data

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Advancements in integrated neuroscience are often characterized with data-driven approaches for discovery; these progressions are the result of continuous efforts aimed at developing integrated frameworks for the investigation of neuronal dynamics at increasing resolution and in varying scales. Since insights from integrated neuronal models frequently rely on both experimental and computational approaches, simulations and data modeling have inimitable roles. Moreover, data sharing across the neuroscientific community has become an essential component of data-driven approaches to neuroscience as is evident from the number and scale of ongoing national and multinational projects, engaging scientists from diverse branches of knowledge. In this heterogeneous environment, the need to share neuroscientific data as well as to utilize it across different simulation environments drove the momentum for standardizing data models for neuronal morphologies, biophysical properties, and connectivity schemes. Here, I review existing data models in neuroinformatics, ranging from flat to hybrid object-hierarchical approaches, and suggest a framework with which these models can be linked to experimental data, as well as to established records from existing databases. Linking neuronal models and experimental results with data on relevant articles, genes, proteins, disease, etc., might open a new dimension for data-driven neuroscience.

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Developing a data sharing community for spinal cord injury research

Cited by 50 publications

References 39 publications

Outcome heterogeneity and bias in acute experimental spinal cord injury

Outcome heterogeneity and bias in acute experimental spinal cord injury

A beginner’s guide to data stewardship and data sharing

Data Models in Neuroinformatics

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