Category: Research

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.

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The “Broken Science” (aka “waste”) debate: A personal cheat sheet

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On March 17, 2015 five panelists from cognitive neuroscience and psychology (Sam Schwarzkopf, Chris Chambers, Sophie Scott, Dorothy Bishop, and Neuroskeptic) publicly debated  “Is science broken? If so, how can we fix it?” . The event was organized by Experimental Psychology, UCL Division of Psychology and Language Sciences / Faculty of Brain Sciences in London.

The debate revolved around the ‘reproducibility crisis’, and covered false positive rates, replication, faulty statistics, lack of power, publication bias, study preregistration, data sharing, peer review, you name it. Understandably the event caused a stir in the press, journals, and the blogosphere (NatureBiomed centralAidan’s AviaryThe Psychologist, etc…).

Remarkably, some of the panelists (notably Sam Schwarzkopf) respectfully opposed the current ‘crusade for true science’  (to which I must confess I subscribe) by arguing that science is not broken at all,  but rather, by trying to fix it we run the risk to wreck it for good. Already a few days before the official debate, he and Neuroskeptic had started to exchange arguments on Neuroskeptic’s blog. While both parties appear to agree that science can be improved, they completely disagree in their analysis of the current status of the scientific enterprise, and consequently also on action points.

This predebate argument directed my attention to a blog, which was run by Sam Schwarzkopf, or rather his alter ego, the ‘Devil’s neuroscientist’ for a short, but very productive period. Curiously, the Devil’s neuroscientist retired from blogging the night before the debate, threatening that there will be no future posts! This is sad, because albeit somewhat aggressively, but very much to the point, the Devil’s neuroscientist tried to debunk the thesis that there is any reproducibility crisis, that science is not self-correcting, that studies should be preregistered, etc. In other words, he was arguing against most of the issues raised and remedies suggested also on my pages. In passing, he provided a lot of interesting links to proponents on either side of the fence. Although I do not agree with many of his conclusions, his is by far the most thoughtful treatment on the subject. Most of the time I discuss with fellow scientist who dismiss problems of the current model of biomedical research I get rather unreflected comments. They usually simply celebrate the status quo as the best of all possible worlds and don’t get beyond the statement that there may be a few glitches, but that the model has evolved over centuries of undeniable progress. “If it’s not broken, don’t fix it.”

The Devil’s blog stimulated me to produce a short summary of key arguments of the current debate, to organize my own thoughts and as a courtesy to the busy reader. Continue reading

“Translational research may be most successful when it fails”: The efficiency of translation in biomedicine

Blühende WüsteIn 2009, Chalmers and Glasziou investigated sources of avoidable waste in biomedical research and estimated that its cumulative effect was that about 85% of research investment is wasted (Lancet 2009; 374: 86–89). Critical voices have since then questioned the exceedingly high number (85%), or claimed that because of non-linearity’s and idiosyncrasies of the biomedical research process a large number of failures are needed to produce a comparably small number of breakthroughs, and therefore hailed the remaining 15%. Waste is defined as ‘resources consumed by inefficient or non-essential activities’. Does progress really thrive on waste?

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Are scientific papers actually read?

wastebinThe MEDLINE currently indexes 5,642 journals. PubMed comprises more than 24 million citations for biomedical literature from MEDLINE. My field is stroke research. Close to 30.000 articles were published in 2014 on the topic ‘Stroke’ (clinical and experimental), more than 20.000 of them were peer reviewed original articles in the English language (Web of Science). That amounts to more than 50 articles every day. In 2014, 1700 of them were rodent studies, a mere 5 per day. Does (can) anyone read them? And should we read them? Do researchers worldwide every day produce knowledge worth publishing in 50 articles?

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