25
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      What exactly is ‘ N’ in cell culture and animal experiments?

      other

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Biologists determine experimental effects by perturbing biological entities or units. When done appropriately, independent replication of the entity–intervention pair contributes to the sample size ( N) and forms the basis of statistical inference. If the wrong entity–intervention pair is chosen, an experiment cannot address the question of interest. We surveyed a random sample of published animal experiments from 2011 to 2016 where interventions were applied to parents and effects examined in the offspring, as regulatory authorities provide clear guidelines on replication with such designs. We found that only 22% of studies (95% CI = 17%–29%) replicated the correct entity–intervention pair and thus made valid statistical inferences. Nearly half of the studies (46%, 95% CI = 38%–53%) had pseudoreplication while 32% (95% CI = 26%–39%) provided insufficient information to make a judgement. Pseudoreplication artificially inflates the sample size, and thus the evidence for a scientific claim, resulting in false positives. We argue that distinguishing between biological units, experimental units, and observational units clarifies where replication should occur, describe the criteria for genuine replication, and provide concrete examples of in vitro, ex vivo, and in vivo experimental designs.

          Related collections

          Most cited references21

          • Record: found
          • Abstract: found
          • Article: not found

          A call for transparent reporting to optimize the predictive value of preclinical research.

          The US National Institute of Neurological Disorders and Stroke convened major stakeholders in June 2012 to discuss how to improve the methodological reporting of animal studies in grant applications and publications. The main workshop recommendation is that at a minimum studies should report on sample-size estimation, whether and how animals were randomized, whether investigators were blind to the treatment, and the handling of data. We recognize that achieving a meaningful improvement in the quality of reporting will require a concerted effort by investigators, reviewers, funding agencies and journal editors. Requiring better reporting of animal studies will raise awareness of the importance of rigorous study design to accelerate scientific progress.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Analysis of serial measurements in medical research.

            In medical research data are often collected serially on subjects. The statistical analysis of such data is often inadequate in two ways: it may fail to settle clinically relevant questions and it may be statistically invalid. A commonly used method which compares groups at a series of time points, possibly with t tests, is flawed on both counts. There may, however, be a remedy, which takes the form of a two stage method that uses summary measures. In the first stage a suitable summary of the response in an individual, such as a rate of change or an area under a curve, is identified and calculated for each subject. In the second stage these summary measures are analysed by simple statistical techniques as though they were raw data. The method is statistically valid and likely to be more relevant to the study questions. If this method is borne in mind when the experiment is being planned it should promote studies with enough subjects and sufficient observations at critical times to enable useful conclusions to be drawn. Use of summary measures to analyse serial measurements, though not new, is potentially a useful and simple tool in medical research.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              A dirty dozen: twelve p-value misconceptions.

              The P value is a measure of statistical evidence that appears in virtually all medical research papers. Its interpretation is made extraordinarily difficult because it is not part of any formal system of statistical inference. As a result, the P value's inferential meaning is widely and often wildly misconstrued, a fact that has been pointed out in innumerable papers and books appearing since at least the 1940s. This commentary reviews a dozen of these common misinterpretations and explains why each is wrong. It also reviews the possible consequences of these improper understandings or representations of its meaning. Finally, it contrasts the P value with its Bayesian counterpart, the Bayes' factor, which has virtually all of the desirable properties of an evidential measure that the P value lacks, most notably interpretability. The most serious consequence of this array of P-value misconceptions is the false belief that the probability of a conclusion being in error can be calculated from the data in a single experiment without reference to external evidence or the plausibility of the underlying mechanism.
                Bookmark

                Author and article information

                Journal
                PLoS Biol
                PLoS Biol
                plos
                plosbiol
                PLoS Biology
                Public Library of Science (San Francisco, CA USA )
                1544-9173
                1545-7885
                4 April 2018
                April 2018
                4 April 2018
                : 16
                : 4
                : e2005282
                Affiliations
                [1 ] Quantitative Biology, Discovery Sciences, AstraZeneca, Cambridge, United Kingdom
                [2 ] School of Physiology, Pharmacology and Neuroscience, University of Bristol, Bristol, United Kingdom
                [3 ] MRC Integrative Epidemiology Unit, School of Experimental Psychology, University of Bristol, Bristol, United Kingdom
                Author notes

                The authors have declared that no competing interests exist.

                Author information
                http://orcid.org/0000-0002-3409-3407
                Article
                pbio.2005282
                10.1371/journal.pbio.2005282
                5902037
                29617358
                8bb42be7-ba2a-4cb8-a5e0-11ad63bb2426
                © 2018 Lazic et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                Page count
                Figures: 3, Tables: 0, Pages: 14
                Funding
                The authors received no specific funding for this work.
                Categories
                Perspective
                Research and Analysis Methods
                Research Design
                Experimental Design
                Biology and Life Sciences
                Cell Biology
                Cellular Types
                Animal Cells
                Neurons
                Biology and Life Sciences
                Neuroscience
                Cellular Neuroscience
                Neurons
                Biology and Life Sciences
                Anatomy
                Head
                Eyes
                Medicine and Health Sciences
                Anatomy
                Head
                Eyes
                Biology and Life Sciences
                Anatomy
                Ocular System
                Eyes
                Medicine and Health Sciences
                Anatomy
                Ocular System
                Eyes
                Research and Analysis Methods
                Experimental Organism Systems
                Inbred Strains
                Research and Analysis Methods
                Biological Cultures
                Cell Cultures
                Biology and Life Sciences
                Biochemistry
                Bioenergetics
                Energy-Producing Organelles
                Mitochondria
                Biology and Life Sciences
                Cell Biology
                Cellular Structures and Organelles
                Energy-Producing Organelles
                Mitochondria
                Biology and Life Sciences
                Organisms
                Eukaryota
                Animals
                Vertebrates
                Amniotes
                Mammals
                Primates
                Medicine and Health Sciences
                Women's Health
                Maternal Health
                Pregnancy
                Medicine and Health Sciences
                Women's Health
                Obstetrics and Gynecology
                Pregnancy
                Custom metadata
                vor-update-to-uncorrected-proof
                2018-04-16

                Life sciences
                Life sciences

                Comments

                Comment on this article