Publication bias is an important problem in laboratory animal research (LAR) according to laboratory animal researchers. We estimate that only fifty percent of LAR is published, but it may be far less in for-profit organizations given that their employees estimated that only ten percent of LAR gets published overall, including their own. Lack of statistical significance, technical problems, the opinions of supervisors and peer reviewers were considered important drivers of non-publication. Respondents thought that mandatory publication of study protocols, research results or the reasons why results could not be obtained may accelerate scientific progress.
To our knowledge, this is the first survey among laboratory animal researchers focusing on publication bias. This survey has several limitations. First, we estimate the response rate to this survey to be between 14 and 24 percent. We do not know to which extent the results are representative for the Dutch LAR community, let alone for the wider LAR community. The number of laboratory animal researchers in The Netherlands is unknown. We were unable to obtain exact information from the institutes on to the number of E-mail addresses to which the survey had been sent. Another difficulty is that such address lists may not always be fully up to date. In particular, researchers who retire or change jobs may be listed in error. Second, the survey was restricted to one country. Third, only few researchers in for-profit organizations participated. Fourth, our results are reminiscent of the joke about surveys on driving ability in which 90% of respondents think that they belong to the group of people whose driving abilities are above-average. Likewise, it seems somewhat paradoxical that our respondents estimate the publication rate of their own work as much higher (in theory, they could have calculated it) than the overall rate. Another explanation may be that the 50% rate mentioned in the introduction to the survey acted as an anchor that made respondents estimate the overall rates as too low. That would imply that a non-publication rate of 20% is closer to the truth. This issue is related to the next one. Fifth, our study investigated researchers’ opinions, which may not reflect the true rate(s) of non-publication. Sixth, due to the large number of statistical significance tests (n
121), application of the Bonferroni correction for multiple testing (at alpha
0.05) implies that only p-values below 0.0004 should be considered statistically significant (see also the legend to Table S1
). The assessment of the effects of the four stratification variables should be considered explorative. Seventh, we were unable to assess the impact of scientific sub-discipline on the results since the free text field (survey item A.1, Appendix S2
) yielded imprecise data with large variation.
Data on non-publication rates in LAR are scarce. Sena et al, using the statistical “trim and fill” technique on a large number of animal experiments on acute ischemic stroke, estimated the non-publication rate to be 13.6 percent which was associated with a 30% overstatement of efficacy. 
Evidence from clinical research on humans suggests that between 46 and 67 percent of studies are not published 
, and that in those published, positive findings are over-emphasized. 
The emergence of trial registration, and the joint statement of the International Committee of Medical Journal Editors on publication of randomized trials being conditional on a trial having a public trial registration number may have reduced these numbers. 
We agree with Sena et al who argued that “non-publication is unethical since it deprives researchers of the accurate data they need to estimate the potential of novel therapies in clinical trials, but also because the included animals are wasted because they do not contribute to accumulating knowledge. In addition, research syntheses that overstate effects may lead to further unnecessary animal experiments testing poorly founded hypotheses.” 
Measures against the suppression of “negative” results can be categorized from the source, via upstream to more downstream measures. Since, in The Netherlands, all experiments must pass a Institutional Animal Care and Use Committee (IACUC) for ethics approval, IACUCs may play a crucial role in the registration of all LAR and prevention of publication bias. A system ensuring periodic follow-up of each experiment’s fate would reinforce such registration. It may be challenging to build a watertight system that simultaneously minimizes bureaucracy. Application of modern information technology may be crucial. One option to prevent that study results have an effect on the editorial decision is to initially submit manuscripts without any results. 
Editors and peer reviewers would judge the importance of submissions through the background, hypotheses and methods sections. This would ensure that acceptance is not conditional on the results. More downstream measures include special journals, journal sections or repositories for “negative” results, such as the Journal of Negative Results in Biomedicine, The All Results Journals and Negative Results in Gynecological Oncology. 
In addition, two journals, the Journal of Cerebral Blood Flow and Metabolism and Neurobiology of Aging, feature Negative Results sections with a very similar flavor. 
The Journal of Cerebral Blood Flow and Metabolism describes this section as follows: “Negative Results is intended to provide a forum for data that did not substantiate [.] a difference between the experimental groups, and/or did not reproduce published findings. Since the net effect of a Negative Result is to discourage repetition, the standards for acceptance as a Negative Result will be highly demanding. Typically, Type II error considerations are mandatory.” 
Statistical significance testing is probably a main driver of non-publication. This is especially unfortunate given the widespread errors involved in understanding the meaning of p-values.
What are the implications for further research? As we have learnt from randomized trials in humans, follow-up of cohorts of study protocols may help us understand the magnitude and the causes of publication bias in LAR, which in turn may affect the research community’s motivation to deal with it. In the meantime, more research into statistical correction of publication bias seems useful.
Specifically, the comparison of various methods to deal with publication bias statistically, such as the trim and fill 
, regression-based methods 
, and capture-recapture 
may be compared in simulation studies to assess their strengths and weaknesses in various situations.