Is Translational Stroke Research Broken, and if So, How Can We Fix It?

 

broken pipeBased on research, mainly in rodents, tremendous progress has been made in our basic understanding of the pathophysiology of stroke. After many failures, however, few scientists today deny that bench-to-bedside translation in stroke has a disappointing track record. I here summarize many measures to improve the predictiveness of preclinical stroke research, some of which are currently in various stages of implementation: We must reduce preventable (detrimental) attrition. Key measures for this revolve around improving preclinical study design. Internal validity must be improved by reducing bias; external validity will improve by including aged, comorbid rodents of both sexes in our modeling. False-positives and inflated effect sizes can be reduced by increasing statistical power, which necessitates increasing group sizes. Compliance to reporting guidelines and checklists needs to be enforced by journals and funders. Customizing study designs to exploratory and confirmatory studies will leverage the complementary strengths of both modes of investigation. All studies should publish their full data sets. On the other hand, we should embrace inevitable NULL results. This entails planning experiments in such a way that they produce high-quality evidence when NULL results are obtained and making these available to the community. A collaborative effort is needed to implement some of these recommendations. Just as in clinical medicine, multicenter approaches help to obtain sufficient group sizes and robust results. Translational stroke research is not broken, but its engine needs an overhauling to render more predictive results.

Read the full article at the Publishers site (STROKE/AHA). If your library does not have a subscription, here is the Authors Manuscript (Stroke/AHA did not allow me to even pay for open access, as it is ‘a special article…’).

The Relative Citation Ratio: It won’t do your laundry, but can it exorcise the journal impact factor?

impactRecently, NIH Scientists  B. Ian Hutchins and colleagues have (pre)published “The Relative Citation Ratio (RCR). A new metric that uses citation rates to measure influence at the article level”. [Note added 9.9.2016: A peer reviewed version of the article has now appeared in PLOS Biol]. Just as Stefano Bertuzzi, the Executive Director of the American Society for Cell Biology, I am enthusiastic about the RCR. The RCR appears to be a viable alternative to the widely (ab)used Journal Impact Factor (JIF).

The RCR has been recently discussed in several blogs and editorials (e.g. NIH metric that assesses article impact stirs debate; NIH’s new citation metric: A step forward in quantifying scientific impact? ). At a recent workshop organized by the National Library of Medicine (NLM) I learned that the NIH is planning to widely use the RCR in its own grant assessments as an antidote to JIF, raw article citations, h-factors, and other highly problematic or outright flawed metrics. Continue reading →

Podcast ‘Vertrauen und Kontrolle in der Wissenschaft’

forschergeist_400x40090-minütiges Gespräch mit Tim Pritlove in dessen Stifterverband Podcast ‘Forschergeist’ zu ‘Vertrauen und Kontrolle in der Wissenschaft: Über die Reproduzierbarkeit von Studien und Methoden zur Qualitätskontrolle’

Gut microbiota impact on stroke outcome: Fad or fact?

gutandbrainMicrobiota and its contribution to brain function and diseases has become a hot topic in neuroscience. In this article in the Journal of Cerebral Blood Flow and Metabolism we discuss the emerging role of commensal bacteria in the course of stroke. Further, we review potential pitfalls in microbiota research and their impact on how we interpret the available evidence, emerging results, and on how we design future studies

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SCI-HUB: The beginning of the end of publishing as we know it?

scihubUnbeknownst to many academics working in large universities or research institutes, SCI-HUB, ‘the first pirate website in the world to provide mass and public access to tens of millions of research papers’, has made almost every research paper worldwide accessible for free. While spoiled academics in rich countries retrieve almost any paper which they are interested in with a simple mouse click via their institution, an illegal operation is serving an even greater selection of articles to those less fortunate who otherwise might have to pay 20 $ or more per article.This act of piracy is threatening the publishing industry to the very foundation – not unlike Napster or Pirate Bay had threatened the music industry. Meanwhile, the publishing industry is still dancing on volcanoes. Double dipping in the transition phase from institutional subscriptions to a per-article fee based Open Access business model (i.e. charging authors AND libraries for giving access to the same article) the publishing giants still generate profit rates of 20 -40%. But what if a growing number of scientists use SCI-HUB as their portal for downloading articles, and courts can’t stop it? Two recent blogs nicely explain how SCI-HUB works, and what it could mean for you and the publishers. One from an industry perspective, the other celebrating Alexandra Elbakyan, the founder of SCI-HUB, as the Robin Hood of Science. If what happened in the music industry is predictive for publishing, it is quite likely that despite an eventual victory of the establishment over the pirates the business model of the industry will have to dramatically change. With unforeseeable consequences for how we are going to publish our research, and how (or even whether) we will be charged for access to publications.

 

 

A pocket guide to electronic laboratory notebooks

labbookEvery professional doing active research in the life sciences is required to keep a laboratory notebook. However, while science has changed dramatically over the last centuries, laboratory notebooks have remained essentially unchanged since pre-modern science. In an article published in F1000Research, an open access post-publication immediate publication platform, we argue that the implementation of electronic laboratory notebooks (eLN) in academic research is overdue, and we provide researchers and their institutions with the background and practical knowledge to select and initiate the implementation of an eLN in their laboratories. In addition, we present data from surveying biomedical researchers and technicians regarding which hypothetical features and functionalities they hope to see implemented in an eLN, and which ones they regard less important. We also present data on acceptance and satisfaction of those who have recently switched from paper laboratory notebook to an eLN.  We thus provide answers to the following questions: What does an electronic laboratory notebook afford a biomedical researcher, what does it require, and how should one go about implementing it? Read the full article

Where have all the rodents gone?

Using metaanalysis and computer simulation we studied the effects of attrition in experimental research on cancer and stroke. The results were published this week in the new meta-research section of PLOS Biology. Not surprisingly, given the small sample sizes of preclinical experimentation, loss of animals in experiments can dramatically alter results. However, effects of attrition on distortion of results were unknown. We used a simulation study to analyze the effects of random and biased attrition. As expected, random loss of samples decreased statistical power, but biased removal, including that of outliers, dramatically increased probability of false positive results. Next, we performed a meta-analysis of animal reporting and attrition in stroke and cancer. Most papers did not adequately report attrition, and extrapolating from the results of the simulation data, we suggest that their effect sizes were likely overestimated. Continue reading →

BIAS!

bias

This has been a week chock-full of bias! First nature ran a cover story on it, with an editorial, and a very nice introduction into the subject by Regina Nuzzo. Then Malcolm Macleod and colleagues published a perspective in Plos Biology demonstrating limited reporting of measures to reduce the risk of bias in life sciences publications, and that there may be an inverse correlation between journal rank or prestige of the University from which the research originated and presence of measures to prevent bias. At the same time Jonathan Kimmelman’s group came out with a report in eLife  in which they meta-analytically explored preclinical studies of an anticancer drug (sunitinib) to demonstrate that only a fraction of drugs that show promise in animals end up proving safe and effective in humans, partly because of design flaws, such as lack of prevention of bias, and partly due to positive publication bias. Both articles resulted in a worldwide media frenzy, including coverage by Nature and the lay press, here is an example from the Guardian. Retraction Watch interviewed Jonathan, while Malcolm spoke on BBC4.

Wenn Forschung nicht hält, was sie verspricht

Eine Sendung dlfdes Deutschlandfunk (ausgestrahlt 20.9.15) von Martin Hubert. Aus der Ankündigung: ‘Biomediziner sollen in ihren Laboren unter anderem nach Substanzen gegen Krebs oder Schlaganfall suchen. Sie experimentieren mit Zellkulturen und Versuchstieren, testen gewollte Wirkungen und ergründen ungewollte. Neuere Studien zeigen jedoch, dass sich bis zu 80 Prozent dieser präklinischen Studien nicht reproduzieren lassen.’ Hier der Link zum Audiostream bzw. zum  Transkript.

(German only, sorry!)

Trust but verify: Institutions must do their part for reproducibility

robust scienceThe crisis in scientific reproducibility has crystalized as it has become increasingly clear that the faithfulness of the majority of high-profile scientific reports is with little foundation, and that the societal burden of low reproducibility is enormous. In todays issue of Nature, C. Glenn Begley, Alastair Buchan, and myself suggest measures by which academic institutions can improve the quality and value of their research. To read the article, click here.

Our main point is that research institutions that receive public funding should be required to demonstrate standards and behaviors that comply with “Good Institutional Practice”. Here is a selection of potential measures, implementation of which shuld be verified, certified and approved by major funding agencies.

Compliance with agreed guidelines:  Ensure compliance with established guidelines such as ARRIVE, MIAME, data access (as required by National Science Foundation and National Institutes of Health, USA).

Full access to the institution’s research results: Foster open access and open data; preregistration of preclinical study designs.

Electronic laboratory notebooks: Provide electronic record keeping compliant with FDA Code of Federal Regulations Title 21 (CFR Title 21 part 11). Electronic laboratory notebooks allow data and project sharing, supervision, time stamping, version control, and directly link records and original data.

Institutional Standard for Experimental Research Conduct (ISERC): Establish ISERC (e.g. blinding, inclusion of controls, replicates and repeats etc); ensure dissemination, training and compliance with IMSERC.

Quality management: Organize regular and random audits of laboratories and departments with reviews of record keeping and measures to prevent bias (such as randomization and blinding).

Critical incidence reporting: Implement a system to allow the anonymous reporting of critical incidences during research. Organize regular critical incidence conferences in which such ‘never events’ are discussed to prevent them in the future and create a culture of research rigor and accountability.

Incentives and disincentives: Develop and implement novel indices to appraise and reward research of high quality.  Honor robustness and mentoring as well as originality of research. Define appropriate penalties for substandard research conduct or noncompliance with guidelines. These might include decreased laboratory space, lack of access to trainees, reduced access to core facilities.

Training:  Establish mandatory programs to train academic clinicians and basic researchers at all professional levels in experimental design, data analysis and interpretation, as well as reporting standards.

Research quality mainstreaming: Bundle established performance measures plus novel  institution-unique measures to allow a flexible, institution-focused algorithm that can serve as the basis for competitive funding applications.

Research review meetings: create forum for routine assessment of institutional publications with focus on robust methods: the process rather than result.

Continue reading →