Can Statistics Cause Brain Damage?

Ford, Ian

doi:10.1038/jcbfm.1983.39

Cited by 12 publications

(4 citation statements)

References 7 publications

(6 reference statements)

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“…Scholarly journals like Stroke or the Journal of Cerebral Blood Flow no longer feature statistical consultants on their editorial boards, and statisticians no longer partake in the scientific discourse (Ford, 1983 Table 4). This apparent lack of attention to statistics is distressing, as it is not uncommon to find violations of statistical standards in published papers in experimental stroke research.…”

Section: Statisticsmentioning

confidence: 99%

“…Scholarly journals like Stroke or the Journal of Cerebral Blood Flow no longer feature statistical consultants on their editorial boards, and statisticians no longer partake in the scientific discourse (Ford, 1983). In addition, instructions for authors do not mention statistics, let alone specific statistical requirements or standards.…”

Section: Statisticsmentioning

confidence: 99%

“…Systematic reviews are an important tool to improve quality in experimental stroke research (Sandercock and Roberts, 2002; Macleod et al , 2005 a , Macleod et al , 2005 d ; Roberts et al , 2002; van der Worp et al , 2005), as they not only, at least partially, overcome the problem of small sample sizes, but they can also deliver quantitative estimates for such confounders as publication bias (e.g., by funnel plot; Sutton et al , 2000) or effect of industry sponsorship on outcome. The CAMARADES (Collaborative Approach to Meta Analysis and Review of Animal Data from Experimental Stroke) collaboration, an initiative of Clinical Neurosciences, University of Edinburgh, UK and the National Stroke Research Institute, Melbourne, Australia, is spearheading the introduction of this approach into experimental stroke research.…”

Section: How Can We Improve Quality In Experimental Research?mentioning

confidence: 99%

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Bench to Bedside: The Quest for Quality in Experimental Stroke Research

Dirnagl

2006

J Cereb Blood Flow Metab

317

241

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Over the past decades, great progress has been made in clinical as well as experimental stroke research. Disappointingly, however, hundreds of clinical trials testing neuroprotective agents have failed despite efficacy in experimental models. Recently, several systematic reviews have exposed a number of important deficits in the quality of preclinical stroke research. Many of the issues raised in these reviews are not specific to experimental stroke research, but apply to studies of animal models of disease in general. It is the aim of this article to review some quality-related sources of bias with a particular focus on experimental stroke research. Weaknesses discussed include, among others, low statistical power and hence reproducibility, defects in statistical analysis, lack of blinding and randomization, lack of quality-control mechanisms, deficiencies in reporting, and negative publication bias. Although quantitative evidence for quality problems at present is restricted to preclinical stroke research, to spur discussion and in the hope that they will be exposed to meta-analysis in the near future, I have also included some quality-related sources of bias, which have not been systematically studied. Importantly, these may be also relevant to mechanism-driven basic stroke research. I propose that by a number of rather simple measures reproducibility of experimental results, as well as the step from bench to bedside in stroke research may be made more successful. However, the ultimate proof for this has to await successful phase III stroke trials, which were built on basic research conforming to the criteria as put forward in this article.

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Section: Statisticsmentioning

confidence: 99%

“…Scholarly journals like Stroke or the Journal of Cerebral Blood Flow no longer feature statistical consultants on their editorial boards, and statisticians no longer partake in the scientific discourse (Ford, 1983). In addition, instructions for authors do not mention statistics, let alone specific statistical requirements or standards.…”

Section: Statisticsmentioning

confidence: 99%

Section: How Can We Improve Quality In Experimental Research?mentioning

confidence: 99%

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Bench to Bedside: The Quest for Quality in Experimental Stroke Research

Dirnagl

2006

J Cereb Blood Flow Metab

317

241

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“…However, a review of the research literature shows that although multiple comparisons are among the most frequently used statistical approaches (Kilkenny et al, 2009), failure to adjust for multiple comparisons is highly prevalent in many fields (Williams et al, 1997;Murphy, 2004). Already in 1983, Ian Ford observed that errors in multiple comparisons were common in the first two volumes of the Journal of Cerebral Blood Flow and Metabolism (Ford, 1983). In a recent systematic analysis of all papers published in 2008 in this journal, we found that this lamentable situation remains unchanged (Deister et al, in preparation).…”

mentioning

confidence: 99%

Statistics in Experimental Cerebrovascular Research: Comparison of More than Two Groups with a Continuous Outcome Variable

Dirnagl

2010

J Cereb Blood Flow Metab

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A common setting in experimental cerebrovascular research is the comparison of more than two experimental groups. Often, continuous measures such as infarct volume, cerebral blood flow, or vessel diameter are the primary variables of interest. This article presents the principles of the statistical analysis of comparing more than two groups using analysis of variance (ANOVA). We will also explain post hoc comparisons, which are required to show which groups significantly differ once ANOVA has rejected the null hypothesis. Although statistical packages perform ANOVA and post hoc contrast at a key stroke, in this study, we use examples from experimental stroke research to reveal the simple math behind the calculations and the basic principles. This will enable the reader to understand and correctly interpret the readout of statistical packages and to help prevent common errors in the comparison of multiple means.

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Critically Producing Brain Images of Mind

Dumit

2011

Critical Neuroscience

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Can Statistics Cause Brain Damage?

Cited by 12 publications

References 7 publications

Bench to Bedside: The Quest for Quality in Experimental Stroke Research

Bench to Bedside: The Quest for Quality in Experimental Stroke Research

Statistics in Experimental Cerebrovascular Research: Comparison of More than Two Groups with a Continuous Outcome Variable

Critically Producing Brain Images of Mind

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