Publication bias in reports of animal stroke studies leads to major overstatement of efficacy - PubMed (original) (raw)

Publication bias in reports of animal stroke studies leads to major overstatement of efficacy

Emily S Sena et al. PLoS Biol. 2010.

Abstract

The consolidation of scientific knowledge proceeds through the interpretation and then distillation of data presented in research reports, first in review articles and then in textbooks and undergraduate courses, until truths become accepted as such both amongst "experts" and in the public understanding. Where data are collected but remain unpublished, they cannot contribute to this distillation of knowledge. If these unpublished data differ substantially from published work, conclusions may not reflect adequately the underlying biological effects being described. The existence and any impact of such "publication bias" in the laboratory sciences have not been described. Using the CAMARADES (Collaborative Approach to Meta-analysis and Review of Animal Data in Experimental Studies) database we identified 16 systematic reviews of interventions tested in animal studies of acute ischaemic stroke involving 525 unique publications. Only ten publications (2%) reported no significant effects on infarct volume and only six (1.2%) did not report at least one significant finding. Egger regression and trim-and-fill analysis suggested that publication bias was highly prevalent (present in the literature for 16 and ten interventions, respectively) in animal studies modelling stroke. Trim-and-fill analysis suggested that publication bias might account for around one-third of the efficacy reported in systematic reviews, with reported efficacy falling from 31.3% to 23.8% after adjustment for publication bias. We estimate that a further 214 experiments (in addition to the 1,359 identified through rigorous systematic review; non publication rate 14%) have been conducted but not reported. It is probable that publication bias has an important impact in other animal disease models, and more broadly in the life sciences.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Figure 1

Figure 1. QUOROM chart of fate of 71 publications identified in systematic search for studies reporting the quantitative impact of publication bias in reports of animal experiments modelling human disease.

Figure 2

Figure 2. Example funnel plots, Egger regressions, and trim-and-fill plots.

Data from meta-analyses of hypothermia (a,e,i), tPA (b,f,j), stem cells (c,g,k), and growth factors (d,h,l). (a–d) Funnel plots showing precision plotted against effect size. In the absence of publication bias the points should resemble an inverted funnel. (e–h) Egger regression showing precision plotted against the standardised effect size. In the absence of publication bias the regression line should pass through the origin. (i–l) Funnel plots showing the data from (a) to (d) in black, and the additional missing studies imputed by trim-and-fill in red.

Figure 3

Figure 3. Plots describing the complete dataset.

Funnel plot (a), Egger regression (b), and trim-and-fill plots (c). See Figure 1 legend for details.

Figure 4

Figure 4. Absolute overstatement of efficacy for the ten interventions identified through trim-and-fill as showing significant publication bias.

The vertical error bars represent the 95% confidence intervals of the estimate. The width of each column reflects the log of the number of contributing experiments.

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