Reproducibility of biomedical research: Quo vadis?

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Russian proverbThe first cause of failure in the clinical development of new drugs is the lack of effectiveness. Thus, in 2011–2012, 56% of 105 new drugs and new indications for already marketed drugs that failed between Phase 2 (exploratory effectiveness trials) and delivery of the registration dossier to regulatory agencies did fail due to the lack of effectiveness.<a class="elsevierStyleCrossRef" href="#bib0215">1</a> This occurs even though the drugs reaching human administration have shown positive results in preclinical research. Should we infer, therefore, that the basic and preclinical research–for example, conducted in cell lines and in laboratory animals–is responsible, at least in part, for this high failure rate in the clinical development of new drugs? It is likely, since an unwritten rule among venture capital companies state that at least 50% of the preclinical studies published by academic researchers, including most prestigious journals, cannot be replicated in laboratories of the biopharmaceutical industry.<a class="elsevierStyleCrossRef" href="#bib0220">2</a> In fact, a common strategy among venture capital companies is to require validation of the results released either by other academic laboratories, or by private companies other than the research group.<a class="elsevierStyleCrossRef" href="#bib0220">2</a> This, however, does not always occur, and in some cases, a series of poorly designed and reported preclinical studies may have led to conducting clinical trials, with no positive results but exposing the participants to unnecessary risks and side effects.<a class="elsevierStyleCrossRef" href="#bib0225">3</a>What do we know today of reproducibility of research in biomedicine?For centuries, science has corrected itself. The scientific method provides a systematic framework for the formulation; as well as refinement of hypotheses by trial and error. This set is based on the ability of researchers to reproduce the results of their experiments, key to have confidence in the accuracy of the results, and, therefore, consider new research. A finding that cannot be replicated loses credibility.Reproducibility can mean either the ability to replicate results–using the same original data–by independent researchers to the initial group of researchers; or the generation of new data supporting the conclusions of the initial experiment. Here we always take the second meaning of the term, unless otherwise indicated. In any case, the biological variability implies that one should not expect every result of previous research to be replicated. However, the same key findings should be confirmed.<a class="elsevierStyleCrossRef" href="#bib0230">4</a>In recent years many papers in general and scientific journals<a class="elsevierStyleCrossRef" href="#bib0235">5</a> have pointed out the lack of reproducibility of biomedical research, which undoubtedly is related to the estimation that 85% of this research has been wasted.<a class="elsevierStyleCrossRef" href="#bib0240">6</a> This, along with studies showing that the number of retracted articles has increased exponentially in recent years,<a class="elsevierStyleCrossRef" href="#bib0245">7</a> has helped public opinion to reduce their level of trust in science: only a minority (36%) of Americans believe that the information from scientists is accurate and reliable.<a class="elsevierStyleCrossRef" href="#bib0250">8</a>Irreproducibility of research involving preclinical studies is particularly relevant since, as already mentioned, positive results may lead to initiate research with human beings. Biopharmaceutical companies interested in the results of an academic preclinical research usually try to reproduce “the experiment” to validate the results published. In this regard, 2 studies alarmed the scientific community. One, conducted by researchers at Bayer showed some results completely consistent with those published in only 20–25% of 67 projects (47 on oncology).<a class="elsevierStyleCrossRef" href="#bib0255">9</a> Subsequently, researchers from Amgen tried to replicate the results of 53 high-impact papers in oncology and they succeeded only in 6 (11%).<a class="elsevierStyleCrossRef" href="#bib0260">10</a> Some of these oncology drugs reach clinical research and are tested in patients with a type of cancer for which the drugs have not really shown preclinical effectiveness. Therefore, this would explain the failures observed in clinical trials. In fact, oncology is the therapeutic area with a highest failure rate in clinical development: oncology products account for 30% of all new drugs that began phase 2 end eventually end up failing.<a class="elsevierStyleCrossRef" href="#bib0215">1</a> The average rate of translation with successful results in animal model to clinical trials in oncology does not reach 8%.<a class="elsevierStyleCrossRef" href="#bib0265">11</a>Research with humans, much more complex and expensive than the pre-clinical studies, often cannot be replicated. Consider, for example, the comparative effectiveness clinical trials and large prospective cohort studies, which require studying hundreds or thousands of participants over long periods. Despite this, there are some data showing that the situation is not much better than the described with preclinical research. Two examples are sufficient. In controlled clinical trials none of the major findings of 52 observational epidemiological studies could be verified.<a class="elsevierStyleCrossRef" href="#bib0270">12</a> Since replicating clinical trials is very costly – and exposing new patients to placebo or interventions known to have a benefit/risk ratio lower than others is unethical – the quality of publications is evaluated reanalyzing the original data. Thus, 35% of the re-analysis of published clinical trials resulted in different results compared with the trials initially published, involving types and number of patients to be treated.<a class="elsevierStyleCrossRef" href="#bib0275">13</a> It should be noted that usually the regulatory agencies require 2 (or more) effectiveness confirmatory trials (phase 3) for the approval of new drugs,<a class="elsevierStyleCrossRef" href="#bib0280">14</a> except for oncology. Both trials, in most cases are virtually identical (on selection criteria, dose and endpoints), which becomes a simultaneous replication.In psychology (and psychiatry) the situation is even more worrying. It is known that the rate of publication of studies with “positive” results (92%) is at the top of all life and social sciences.<a class="elsevierStyleCrossRef" href="#bib0285">15</a> In 2011 a unique replication process of 100 psychology studies published in 2008 was initiated by 270 psychologists from around the world. First, the experimental design replication of each study was published with the participation of the authors of the original study, if they deemed it necessary. Then, open expert reviews were received of each study, and only then the results were publicly reported.<a class="elsevierStyleCrossRef" href="#bib0290">16</a> Although 97% of the original 100 studies had statistically significant results (p<0.05), including the repeated ones, only in 47% the results could be replicated. In addition, the magnitude of the average effect between the replicated studies was half of the original ones.<a class="elsevierStyleCrossRef" href="#bib0295">17</a> It should be noted, however, that, as Nosek, the driver of this replication study, states: with the data obtained we cannot know which study, the original or the replicated, obtained the actual results.<a class="elsevierStyleCrossRef" href="#bib0300">18</a> A similar study is being conducted to replicate 50 similar oncology and high-impact preclinical studies published.<a class="elsevierStyleCrossRef" href="#bib0305">19</a>Causes of lack of reproducibility of the biomedical researchCurrently, it is estimated that the accumulated irreproducibility rate in preclinical research is up to 53%.<a class="elsevierStyleCrossRef" href="#bib0310">20</a> The major reasons of lack of reproducibility of research include: (a) errors in study design; (b) problems in biological reagents and reference materials, and (c) the biases and errors in analysis and report of results.For example, 2671 studies published from 1991 to 2011, belonging to systematic reviews of the effectiveness of drugs in 8 animal models, an improvement in the percentage of randomization was observed, rising from 14 to 42%–and blinding, which increased from 16 to 39%–within the years shown, respectively. However, the sample calculation did not vary, from 2 to 1%<a class="elsevierStyleCrossRef" href="#bib0315">21</a> (<a class="elsevierStyleCrossRef" href="#tbl0005">Table 1</a>). In 49 meta-analysis (which included 730 studies) in neurosciences, estimating the effect size of interest, it was observed that the mean statistical power was only 21%: it reached 8% in studies of nuclear magnetic resonance imaging, while in animal studies in water maze or radial maze it was set at 18 and 31%, respectively.<a class="elsevierStyleCrossRef" href="#bib0325">23</a> This is an important field for false positive results (<a class="elsevierStyleCrossRef" href="#tbl0005">Table 1</a>). Even though animal models have obvious biological differences with humans, there is a high number of publications with “overoptimistic” conclusions in relation to the effectiveness of the drugs tested: 80% of the effective and safe treatments in animal models fail in clinical trials.<a class="elsevierStyleCrossRef" href="#bib0335">25</a><elsevierMultimedia ident="tbl0005"></elsevierMultimedia>It is estimated that 16–36% of the cell lines used in cancer research – sometimes poorly featured – are contaminated with cells from different types of cancer, being one of the factors with most influence in the irreproducibility of oncology preclinical studies.<a class="elsevierStyleCrossRef" href="#bib0340">26</a> This leads researchers to inadvertently publish promising results of oncological agents against certain cancers in cell lines that, in fact, might correspond to other types of neoplasm. This might be prevented by acquiring cell lines from a reliable provider and verifying them at the researcher's laboratory.It is known that antibodies – one of the reagents most commonly used in biological research – may also be an important irreproducibility source in trials that have been successfully designed and conducted. The use of poorly characterized antibodies, which have not been properly validated or have not been verified by the researchers before conducting the research of interest, can lead to promising results but ultimately useless if not reproducible. In an analysis of 49 commercial antibodies directed to 19 specific receptors, it was found that most of them recognized more than one protein, which was not reliable for distinguishing among multiple receptors. In addition, a study showed that 25% of 246 antibodies used in epigenetic failed the specificity tests.<a class="elsevierStyleCrossRef" href="#bib0345">27</a>In preclinical research, analysis procedures not included in the initial protocol are frequently used, so that the data processing is modified. Thus, the following is not rare: interim analysis is conducted, ad hoc data are excluded, the primary evaluation endpoint is selected post hoc and pseudoreplications are conducted – where technical replication is mistaken with biological replication.<a class="elsevierStyleCrossRef" href="#bib0225">3</a>Publication bias and selective reporting bias of results is well known in all types of research, including clinical trials<a class="elsevierStyleCrossRef" href="#bib0350">28</a>–where various corrective measures have been suggested.<a class="elsevierStyleCrossRef" href="#bib0355">29</a> The main gauge of a successful academic career is the number of publications in high-impact journals. This means that the publication instead of being a means becomes a purpose. Therefore, the need to publish soon may hamper a balanced interpretation of the results. The facts show that this situation frequently causes ‘positive’ results published before they can be verified and reproduced.<a class="elsevierStyleCrossRefs" href="#bib0320">22,30</a>Finally, it is estimated that 49% of all funding (public and private) for biomedical research in the USA is invested in pre-clinical research, reaching 56,000 million dollars/year. With a 50% irreproducibility cumulative rate it is estimated that annually about 28,000 million dollars are invested in studies that cannot be replicated.<a class="elsevierStyleCrossRef" href="#bib0310">20</a>Preventing publication of irreproducible studiesFor the scientific community to recover all its prestige, we must ensure that scientific research is conducted with integrity, with initiatives and measures throughout the whole research project, from conception to reporting the results.<a class="elsevierStyleCrossRefs" href="#bib0330">24,31</a> The most rational way to prevent non-reproducible studies from being published is to be conducted with the utmost rigor. In addition, the journals should require the materials and methods used to be described in detail. This will help prove that design errors (<a class="elsevierStyleCrossRef" href="#tbl0005">Table 1</a>) have not been committed, and, that the analysis of the data was performed clearly, allowing control biases leading many researchers to describe what they want find and disregard what the data really shows.<a class="elsevierStyleCrossRefs" href="#bib0370">32,33</a> As shown below, the pre-clinical research studies will be performed and analyzed with the same quality standards required for clinical research for many years. Thus, in evaluating a paper, Begley<a class="elsevierStyleCrossRef" href="#bib0360">30</a> outlines six aspects that should be reported in the text and they are listed in <a class="elsevierStyleCrossRef" href="#tbl0010">Table 2</a>.<elsevierMultimedia ident="tbl0010"></elsevierMultimedia>Recently, to limit publication bias in animal model, it has been suggested that these studies be registered before initiation.<a class="elsevierStyleCrossRef" href="#bib0380">34</a> However, before that, with the aim of improving standards to publishing animal studies, the ARRIVE<a class="elsevierStyleCrossRef" href="#bib0385">35</a> guidelines was agreed, which was backed by high prestige funders and journals, but unfortunately it did not cause the expected effect.<a class="elsevierStyleCrossRef" href="#bib0390">36</a>The National Institutes of Health (NIH) of the USA–the most important pre-clinical science funding agency in the world–has admitted that the irreproducibility problem affects preclinical research mainly.<a class="elsevierStyleCrossRef" href="#bib0395">37</a> Therefore, NIH has published a guide on how this type of research should be reported, supported by scientific societies, Nature Publishing Group, Science Journals, PLOS Journals, The BMJ and more than 30 journals<a class="elsevierStyleCrossRef" href="#bib0400">38</a> (<a class="elsevierStyleCrossRef" href="#tbl0015">Table 3</a>). As the NIH is aware that the background problem is poor training of researchers, they are developing a specific training module. They are also evaluating whether or not to add a requirement for independent validation of the previous preclinical studies to apply for support for clinical trials.<a class="elsevierStyleCrossRef" href="#bib0395">37</a><elsevierMultimedia ident="tbl0015"></elsevierMultimedia>The near futureAll actors involved in scientific research should contribute to changing the situation in a reasonable time to allow us state that the vast majority of scientific studies in biomedicine are properly conducted and reported, and are reproducible. This is key in the scientific method.As aforementioned, professional and academic career of researchers is based on publishing studies, as soon as possible, and with positive results, in high impact journals. This allows the researcher to have easier access to new support for their projects and therefore new publications, and eventually obtain academic and professional recognition. Some people claim changing the focus of interest of the academic career of researchers and rewarding aspects of scientific activity currently ignored. For example, becoming a reviewer of manuscripts and projects, providing protocol access to third parties and data from studies, training young researchers and replicating previous studies.<a class="elsevierStyleCrossRefs" href="#bib0330">24,37,39</a> This is not about not taking into account the publication of the results, main instrument of evaluation. However, its current extraordinary significance should be reduced since it involves all the scientific activity. In any case, research institutions should promote the “culture of replication”.<a class="elsevierStyleCrossRef" href="#bib0330">24</a> This, however, is not easy to achieve when, currently, support to novel research on reproducibility comes first.<a class="elsevierStyleCrossRef" href="#bib0235">5</a>As already mentioned, the scientific journals and the NIH have taken important decisions that are expected to take effect. But also many other funding agencies should consider to ensure the reproducibility of the research they fund a priority. Similarly, research institutions and universities have major responsibility in this regard.<a class="elsevierStyleCrossRefs" href="#bib0330">24,37,39</a> It has been shown that the 5 most prestigious institutions in the UK in animal model fail miserably to implement methods to limit the risk of scientific bias.<a class="elsevierStyleCrossRef" href="#bib0315">21</a> The centers should train the researchers properly and supervise the integrity of the research conducted at their facilities as well as establish certain performance standards.<a class="elsevierStyleCrossRef" href="#bib0405">39</a> It is likely that this takes a long time to arrive in our country. While this happens, every research center and university should publish a code of good research practice<a class="elsevierStyleCrossRefs" href="#bib0410">40,41</a> and be provided with a committee promoting scientific integrity and evaluate cases of suspected lack of integrity. Every institution should verify that any trainee researcher would receive the training necessary to internalize the values of scientific integrity<a class="elsevierStyleCrossRef" href="#bib0420">42</a> in addition to acquire proper training in methodology and statistics. It seems clear that it is time for biomedical research to “trust, but verify.”Conflicts of interestThe authors report no conflict of interest." "textoCompletoSecciones" => array:1 [ "secciones" => array:6 [ 0 => array:2 [ "identificador" => "sec0005" "titulo" => "What do we know today of reproducibility of research in biomedicine?" ] 1 => array:2 [ "identificador" => "sec0010" "titulo" => "Causes of lack of reproducibility of the biomedical research" ] 2 => array:2 [ "identificador" => "sec0015" "titulo" => "Preventing publication of irreproducible studies" ] 3 => array:2 [ "identificador" => "sec0020" "titulo" => "The near future" ] 4 => array:2 [ "identificador" => "sec0025" "titulo" => "Conflicts of interest" ] 5 => array:1 [ "titulo" => "References" ] ] ] "pdfFichero" => "main.pdf" "tienePdf" => true "fechaRecibido" => "2015-11-03" "fechaAceptado" => "2015-11-23" "NotaPie" => array:1 [ 0 => array:2 [ "etiqueta" => "☆" "nota" => "Please cite this article as: Dal-Ré R, Bernad A, Garesse R. La reproducibilidad de las investigaciones biomédicas: Quo vadis? Med Clin (Barc). 2016;146:408–412." ] ] "multimedia" => array:3 [ 0 => array:8 [ "identificador" => "tbl0005" "etiqueta" => "Table 1" "tipo" => "MULTIMEDIATABLA" "mostrarFloat" => true "mostrarDisplay" => false "detalles" => array:1 [ 0 => array:3 [ "identificador" => "at1" "detalle" => "Table " "rol" => "short" ] ] "tabla" => array:2 [ "leyenda" => "Source: Ioannidis,<a class="elsevierStyleCrossRef" href="#bib0320">22</a> Button et al.<a class="elsevierStyleCrossRef" href="#bib0325">23</a> and Ionnidis et al.<a class="elsevierStyleCrossRef" href="#bib0330">24</a>PPV: positive predictive value.Statistical power: the probability of rejecting correctly the null hypothesis when it is false; i.e. the probability of not making a type 2 or false negative error.Effect size: a standardized measure quantifying the size of the difference between 2 groups or the strength of an association between two variables.Positive predictive value: the probability that a positive result reflects a real, true effect, i.e. it is a true positive finding." "tablatextoimagen" => array:1 [ 0 => array:2 [ "tabla" => array:1 [ 0 => """ <table border="0" frame="\n \t\t\t\t\tvoid\n \t\t\t\t" class=""><tbody title="tbody"><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">Sample size: the smaller size, the more difficult is to obtain a true result. A small sample size implies low statistical power. A low statistical power implies a low probability of finding real, true effects, and facilitates finding false positives. \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">Effect size: the smaller the size of the effect, the less likely that the effect is real. The statistical power is also related to the effect size. With a low statistical power, most likely, the effect size reported is too high. In addition, several studies with low statistical power cause “vibration effect”, i.e. a wide range in the magnitude of the effects found in the study variable. When, as currently, most biomedical research reports small effect sizes, it is more likely that many false positive findings are reported. In fact, obtaining a very large effect size should be observed with caution, as it may be indicative of a significant bias. \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">Relationship between finding and data prior to the study: the probability that a finding is real, true (i.e., the PPV), is strongly influenced by the probability before the study. A randomized controlled trial Phase 3 (confirming effectiveness) properly conducted, with an initial 50% chance of real results and a statistical power of 80%, it has a 85% PPV. That means that in 85% of cases the result obtained in the trial is true. However, a randomized controlled trial Phase 2 (testing effectiveness) properly conducted, with an initial 20% chance of real results and a 20% statistical power, it has a 23% PPV. That means that in 77% of cases the result obtained in the trial is false. \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">Defining endpoints: the higher flexibility in the definition of variables, the less likely that the results are true. Thus, the results of a clinical trial are more likely to be real when well-defined variables are used (e.g., death) compared to using variables with some degree of interpretation, for example, rating scales, often used in psychiatry. Variables with high degree of subjectivity favor the “vibration” effect, depending on the statistical analysis. \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">Validity of endpoints: Valid variables are clinically relevant for the disease under study, which is particularly important when clinical rating scales or composite variables are used. \t\t\t\t\t\t\n \t\t\t\t</td></tr></tbody></table> """ ] "imagenFichero" => array:1 [ 0 => "xTab1200903.png" ] ] ] ] "descripcion" => array:1 [ "en" => "Some of the mistakes in the design of scientific studies." ] ] 1 => array:8 [ "identificador" => "tbl0010" "etiqueta" => "Table 2" "tipo" => "MULTIMEDIATABLA" "mostrarFloat" => true "mostrarDisplay" => false "detalles" => array:1 [ 0 => array:3 [ "identificador" => "at2" "detalle" => "Table " "rol" => "short" ] ] "tabla" => array:3 [ "leyenda" => "Source: Begley.<a class="elsevierStyleCrossRef" href="#bib0360">30</a>" "tablatextoimagen" => array:1 [ 0 => array:2 [ "tabla" => array:1 [ 0 => """ <table border="0" frame="\n \t\t\t\t\tvoid\n \t\t\t\t" class=""><thead title="thead"><tr title="table-row"><th class="td" title="table-head " align="left" valign="top" scope="col" style="border-bottom: 2px solid black">Factor \t\t\t\t\t\t\n \t\t\t\t</th><th class="td" title="table-head " align="left" valign="top" scope="col" style="border-bottom: 2px solid black">Remark \t\t\t\t\t\t\n \t\t\t\t</th></tr></thead><tbody title="tbody"><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">Blinding experiments \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">In vitro studies in animal model and gel reading should be blinded or, at least, the evaluator should be blinded. \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">Repeating experiments \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">Before publishing the results, the experiments should be repeated, something that is only performed in exceptional cases. \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">Reporting the results \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">We should avoid selective publication of results, which is hardly detectable by the reviewers. The images can be manipulated digitally. Therefore, they should be verified. \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">Control groups \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">The results obtained in the control group should always be reported, which is not always carried out. \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">Validating reagents<a class="elsevierStyleCrossRef" href="#tblfn0005">a</a> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">It is crucial to know that, for example, antibodies detect only the antigen of interest. \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">Proper statistical analysis \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="left" valign="top">The use of improper statistical evidence is frequent in animal model. \t\t\t\t\t\t\n \t\t\t\t</td></tr></tbody></table> """ ] "imagenFichero" => array:1 [ 0 => "xTab1200902.png" ] ] ] "notaPie" => array:1 [ 0 => array:3 [ "identificador" => "tblfn0005" "etiqueta" => "a" "nota" => "Any other material can be added here, for example, cell lines, of crucial importance in cancer research (see text)." ] ] ] "descripcion" => array:1 [ "en" => "Points to be verified in evaluating preclinical research papers to ensure the consistency of the results reported." ] ] 2 => array:8 [ "identificador" => "tbl0015" "etiqueta" => "Table 3" "tipo" => "MULTIMEDIATABLA" "mostrarFloat" => true "mostrarDisplay" => false "detalles" => array:1 [ 0 => array:3 [ "identificador" => "at3" "detalle" => "Table " "rol" => "short" ] ] "tabla" => array:2 [ "leyenda" => "Source: National Institutes of Health.<a class="elsevierStyleCrossRef" href="#bib0400">38</a>" "tablatextoimagen" => array:1 [ 0 => array:2 [ "tabla" => array:1 [ 0 => """ <table border="0" frame="\n \t\t\t\t\tvoid\n \t\t\t\t" class=""><tbody title="tbody"><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">1. Rigorous statistical analysis. The journal should be provided with a mechanism to verify the statistical accuracy of the manuscripts \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">2. Transparency. Journals should not impose limits on the extension of the “methods” section. The journals will be provided with an editorial process that ensures reporting to reviewers and readers of key information on the methodology and analysis \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">Guidelines for a rigorous report \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">Standards. Using accepted standards such as ARRIVE<a class="elsevierStyleCrossRef" href="#bib0385">35</a> \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">Replications. Report how many times each experiment was replicated and if the results were verified by repetition and under what conditions \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">Statistics. Require that all statistical accuracy be reported, including the type of analysis, the exact value of No., the definition of center and dispersion and accuracy measures \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">Randommization. For all animal studies, report, at least, whether or not the samples were randomized and the method used \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">Blinding. For all animal studies, report, at least, whether or not the researchers were blinded to group assignment and evaluation \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">Sample estimation. What statistical method was used in the sample estimation \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">Inclusion and exclusion criteria. Any criterion to exclude any data or subject should be reported, and every experimental result omitted from the manuscript for whatever reason should be included \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">3. Access to data and material. All datasets (datasets) in which the conclusions of the manuscript are based should be available to the petitioner (when ethically appropriate) during his evaluation (by the director of the journal and reviewers) and after reasonable request immediately after its publication. The following recommendation should be followed: place the data set in repositories of public access and share software (must be informed if this is available and how to obtain it) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">4. Consideration of rebuttals. When releasing a paper, the journal accepts the responsibility to consider for publication the refutations of that work, which must meet regular quality standards \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">5. Consider best practice guidelines for: \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">Data grounded in images (e.g. Western blots) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">Description of biological material that identifies unmistakably the reagents, in particular, antibodies, cell lines and animals \t\t\t\t\t\t\n \t\t\t\t</td></tr></tbody></table> """ ] "imagenFichero" => array:1 [ 0 => "xTab1200901.png" ] ] ] ] "descripcion" => array:1 [ "en" => "(Summary) Principles and guidelines for repoting preclinical research." ] ] ] "bibliografia" => array:2 [ "titulo" => "References" "seccion" => array:1 [ 0 => array:2 [ "identificador" => "bibs0005" "bibliografiaReferencia" => array:42 [ 0 => array:3 [ "identificador" => "bib0215" "etiqueta" => "1" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "Trial watch: phase II and phase III attrition rates 2011–2012" "autores" => array:1 [ 0 => array:2 [ "etal" => false "autores" => array:2 [ 0 => "J. 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