Primary Research Species of Survey Participants.

<p>Primary Research Species of Survey Participants.</p

Participants’ Assessment of Different Types of Experimental Biases.

<p>Participants’ Assessment of Different Types of Experimental Biases.</p

Original Data of Reichlin et al PLoS One 2016

Reproducibility in animal research is alarmingly low, and a lack of scientific rigor has been proposed as a major cause. Systematic reviews found low reporting rates of measures against risks of bias (e.g., randomization, blinding), and a correlation between low reporting rates and overstated treatment effects. Reporting rates of measures against bias are thus used as a proxy measure for scientific rigor, and reporting guidelines (e.g., ARRIVE) have become a major weapon in the fight against risks of bias in animal research. Surprisingly, animal scientists have never been asked about their use of measures against risks of bias and how they report these in publications. Whether poor reporting reflects poor use of such measures, and whether reporting guidelines may effectively reduce risks of bias has therefore remained elusive. To address these questions, we asked in vivo researchers about their use and reporting of measures against risks of bias and examined how self-reports relate to reporting rates obtained through systematic reviews. An online survey was sent out to all registered in vivo researchers in Switzerland (N=1891) and was complemented by personal interviews with five representative in vivo researchers to facilitate interpretation of the survey results

List of measures against risks of bias included in this study.

<p>List of measures against risks of bias included in this study.</p

Boxplot of IVS versus certification of institution.

<p>Comparison of IVS for (A) experimental conduct, and (B) for the reporting in publications with respect to working institutions being certified. Mean IVS are slightly but non-significantly higher for participants working for certified institutions.</p

Prevalence of the measures used and reported to avoid risks of bias by the participants to online survey.

<p>(A) Prevalence of use of bias avoidance measures during experimental conduct and reporting in the participants’ latest publication (percentages are corrected for ‘no answer’, ‘does not apply to last manuscript’ and ‘have not published so far’. (B) Internal validity scores (IVS) for experimental conduct and reporting in publications.</p

Knowledge of ARRIVE Guidelines by participants to the online survey.

<p>Shown are absolute numbers of participants being ‘familiar’ with the guidelines, ‘having read’ and ‘having heard of’ them, and having ‘never heard of’ the guidelines, split according to the participants’ affiliations (private institutions, pharma, governmental institutions and academia).</p

Boxplot of IVS versus descriptors of model selection process.

<p>Descriptors are selected for the models with lowest BIC, thus best explaining the variation in IVS for (A) experimental conduct and (B) for publications. For (A) one value is missing as no IVS could be calculated, and for (B) 41 values are missing, because participants ticked ‘have not yet published’, gave ‘no answer’ or declared that these questions ‘do not apply to last manuscript’. For experimental conduct (A), the model including ARRIVE knowledge and research field best explained the IVS_Exp, whereas for publications (B), the model including only ARRIVE knowledge best explained the IVS_Pub. Red squares indicate the mean IVS; black circle the mean of IVS_Exp of participants with ARRIVE knowledge; grey triangle the mean IVS_Exp of participants without ARRIVE knowledge. Whiskers are 1.5*interquartile range.</p

Experimental biases and measures to avoid them.

<p>Bars indicate percentage of participants (y-axis) giving that answer (corrected for participants choosing ‘no answer’), red bars indicate effective measures to avoid a given bias (A-F), respectively. The red circle is indicative of the mean of effective measures (<i>sensu stricto</i> according to <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0165999#pone.0165999.t001" target="_blank">Table 1</a>), while the grey rectangle is the mean of ineffective measures. Number of participants answering to questions: attrition bias N = 172; detection bias N = 224; performance bias N = 212; publication bias N = 180; selective reporting N = 213; selection bias N = 219. The list of possible answers (x-axis) included: AlloCon = allocation concealment; Blind = blinding; In / Excl = inclusion / exclusion criteria; PriOut = primary outcome variable; IndRep = independent replication; ITT = Intention-to-Treat analysis; SSCal = sample size calculation; AllRes = reporting of all results; PubHI = publishing in high impact journals; Rand = randomization; Other = other measures.</p

Definitions of key terms used in this manuscript.

<p>Definitions of key terms used in this manuscript.</p