We are analyzing https://link.springer.com/article/10.1186/1471-2202-14-37.

Title:
Improving basic and translational science by accounting for litter-to-litter variation in animal models | BMC Neuroscience
Description:
Background Animals from the same litter are often more alike compared with animals from different litters. This litter-to-litter variation, or “litter effects”, can influence the results in addition to the experimental factors of interest. Furthermore, sometimes an experimental treatment can only be applied to whole litters rather than to individual offspring. An example is the valproic acid (VPA) model of autism, where VPA is administered to pregnant females thereby inducing the disease phenotype in the offspring. With this type of experiment the sample size is the number of litters and not the total number of offspring. If such experiments are not appropriately designed and analysed, the results can be severely biased as well as extremely underpowered. Results A review of the VPA literature showed that only 9% (3/34) of studies correctly determined that the experimental unit (n) was the litter and therefore made valid statistical inferences. In addition, litter effects accounted for up to 61% (p <0.001) of the variation in behavioural outcomes, which was larger than the treatment effects. In addition, few studies reported using randomisation (12%) or blinding (18%), and none indicated that a sample size calculation or power analysis had been conducted. Conclusions Litter effects are common, large, and ignoring them can make replication of findings difficult and can contribute to the low rate of translating preclinical in vivo studies into successful therapies. Only a minority of studies reported using rigorous experimental methods, which is consistent with much of the preclinical in vivo literature.
Website Age:
28 years and 1 months (reg. 1997-05-29).

Matching Content Categories {📚}

Education
Science
Pets

Content Management System {📝}

What CMS is link.springer.com built with?

Custom-built

No common CMS systems were detected on Link.springer.com, and no known web development framework was identified.

Traffic Estimate {📈}

What is the average monthly size of link.springer.com audience?

🌠 Phenomenal Traffic: 5M - 10M visitors per month

Based on our best estimate, this website will receive around 5,000,019 visitors per month in the current month.
However, some sources were not loaded, we suggest to reload the page to get complete results.

check SE Ranking
check Ahrefs
check Similarweb
check Ubersuggest
check Semrush

How Does Link.springer.com Make Money? {💸}

We see no obvious way the site makes money.

Not every website is profit-driven; some are created to spread information or serve as an online presence. Websites can be made for many reasons. This could be one of them. Link.springer.com might be plotting its profit, but the way they're doing it isn't detectable yet.

Keywords {🔍}

litter, animals, litters, pubmed, article, studies, google, scholar, experimental, data, analysis, effects, number, power, animal, results, cas, design, vpa, model, size, variation, treatment, offspring, statistical, research, models, figure, sample, study, values, experiments, unit, effect, differences, addition, literature, false, file, autism, authors, additional, central, littertolitter, preclinical, methods, quality, reporting, published, individual,

Topics {✒️}

org/products/guidelines/safety/safety-single/article/detection article download pdf van der worp mglur5-antagonist mediated reversal 2-methyl-6-phenylethyl-pyrididine sod1 ieno en avoids artificially inflating related subjects article lazic privacy choices/manage cookies studies demonstrating litter-effects festing mfw mixed-effects model numerous incorrect decisions 2-methyl-6-phenylethyl-pyrididine literature search mglur5 receptor antagonist �valproic acid’ [tiab] post hoc flexibility 1038/nrd3439-c1 lazic se open field test mixed effects models full access biomed central potential drug targets search term “ obtain false positives measuring blood pressure require careful planning psychology journals mixed-effect model publishing raw data accelerating drug discovery drug candidates derailed similar content therapeutic compound versus standard murine model single liver sample rigorous experimental methods behav res methods false negative rates false positive rate authors’ original file neurodevelopmental disorders smaller true signals phase ii/iii alzheimers res ther vesterinen hm parkinsonism relat disord

Questions {❓}

Bart van der Worp H, Howells DW, Sena ES, Porritt MJ, Rewell S, O’Collins V, Macleod MR: Can animal models of disease reliably inform human studies?
Bebarta V, Luyten D, Heard K: Emergency medicine animal research: does use of randomization and blinding affect the results?
How does litter-to-litter variation arise?
Lazic SE: The problem of pseudoreplication in neuroscientific studies is it affecting your analysis?
Prinz F, Schlange T, Asadullah K: Believe it or not: how much can we rely on published data on potential drug targets?
Delta?

Schema {🗺️}

WebPage:
      mainEntity:
         headline:Improving basic and translational science by accounting for litter-to-litter variation in animal models
         description:Animals from the same litter are often more alike compared with animals from different litters. This litter-to-litter variation, or “litter effects”, can influence the results in addition to the experimental factors of interest. Furthermore, sometimes an experimental treatment can only be applied to whole litters rather than to individual offspring. An example is the valproic acid (VPA) model of autism, where VPA is administered to pregnant females thereby inducing the disease phenotype in the offspring. With this type of experiment the sample size is the number of litters and not the total number of offspring. If such experiments are not appropriately designed and analysed, the results can be severely biased as well as extremely underpowered. A review of the VPA literature showed that only 9% (3/34) of studies correctly determined that the experimental unit (n) was the litter and therefore made valid statistical inferences. In addition, litter effects accounted for up to 61% (p <0.001) of the variation in behavioural outcomes, which was larger than the treatment effects. In addition, few studies reported using randomisation (12%) or blinding (18%), and none indicated that a sample size calculation or power analysis had been conducted. Litter effects are common, large, and ignoring them can make replication of findings difficult and can contribute to the low rate of translating preclinical in vivo studies into successful therapies. Only a minority of studies reported using rigorous experimental methods, which is consistent with much of the preclinical in vivo literature.
         datePublished:2013-03-22T00:00:00Z
         dateModified:2013-03-22T00:00:00Z
         pageStart:1
         pageEnd:11
         license:http://creativecommons.org/licenses/by/2.0
         sameAs:https://doi.org/10.1186/1471-2202-14-37
         keywords:
            Autism
            Experimental design
            Litter-effects
            Mixed-effects model
            Multiparous
            Nested model
            Valproic acid
            Neurosciences
            Neurobiology
            Animal Models
         image:
            https://media.springernature.com/lw1200/springer-static/image/art%3A10.1186%2F1471-2202-14-37/MediaObjects/12868_2012_Article_2893_Fig1_HTML.jpg
            https://media.springernature.com/lw1200/springer-static/image/art%3A10.1186%2F1471-2202-14-37/MediaObjects/12868_2012_Article_2893_Fig2_HTML.jpg
            https://media.springernature.com/lw1200/springer-static/image/art%3A10.1186%2F1471-2202-14-37/MediaObjects/12868_2012_Article_2893_Fig3_HTML.jpg
            https://media.springernature.com/lw1200/springer-static/image/art%3A10.1186%2F1471-2202-14-37/MediaObjects/12868_2012_Article_2893_Fig4_HTML.jpg
         isPartOf:
            name:BMC Neuroscience
            issn:
               1471-2202
            volumeNumber:14
            type:
               Periodical
               PublicationVolume
         publisher:
            name:BioMed Central
            logo:
               url:https://www.springernature.com/app-sn/public/images/logo-springernature.png
               type:ImageObject
            type:Organization
         author:
               name:Stanley E Lazic
               affiliation:
                     name:Novartis Institutes for Biomedical Research
                     address:
                        name:In Silico Lead Discovery, Novartis Institutes for Biomedical Research, Switzerland
                        type:PostalAddress
                     type:Organization
               email:[email protected]
               type:Person
               name:Laurent Essioux
               affiliation:
                     name:F. Hoffmann-La Roche
                     address:
                        name:Bioinformatics and Exploratory Data Analysis, F. Hoffmann-La Roche, Basel, Switzerland
                        type:PostalAddress
                     type:Organization
               type:Person
         isAccessibleForFree:1
         type:ScholarlyArticle
      context:https://schema.org
ScholarlyArticle:
      headline:Improving basic and translational science by accounting for litter-to-litter variation in animal models
      description:Animals from the same litter are often more alike compared with animals from different litters. This litter-to-litter variation, or “litter effects”, can influence the results in addition to the experimental factors of interest. Furthermore, sometimes an experimental treatment can only be applied to whole litters rather than to individual offspring. An example is the valproic acid (VPA) model of autism, where VPA is administered to pregnant females thereby inducing the disease phenotype in the offspring. With this type of experiment the sample size is the number of litters and not the total number of offspring. If such experiments are not appropriately designed and analysed, the results can be severely biased as well as extremely underpowered. A review of the VPA literature showed that only 9% (3/34) of studies correctly determined that the experimental unit (n) was the litter and therefore made valid statistical inferences. In addition, litter effects accounted for up to 61% (p <0.001) of the variation in behavioural outcomes, which was larger than the treatment effects. In addition, few studies reported using randomisation (12%) or blinding (18%), and none indicated that a sample size calculation or power analysis had been conducted. Litter effects are common, large, and ignoring them can make replication of findings difficult and can contribute to the low rate of translating preclinical in vivo studies into successful therapies. Only a minority of studies reported using rigorous experimental methods, which is consistent with much of the preclinical in vivo literature.
      datePublished:2013-03-22T00:00:00Z
      dateModified:2013-03-22T00:00:00Z
      pageStart:1
      pageEnd:11
      license:http://creativecommons.org/licenses/by/2.0
      sameAs:https://doi.org/10.1186/1471-2202-14-37
      keywords:
         Autism
         Experimental design
         Litter-effects
         Mixed-effects model
         Multiparous
         Nested model
         Valproic acid
         Neurosciences
         Neurobiology
         Animal Models
      image:
         https://media.springernature.com/lw1200/springer-static/image/art%3A10.1186%2F1471-2202-14-37/MediaObjects/12868_2012_Article_2893_Fig1_HTML.jpg
         https://media.springernature.com/lw1200/springer-static/image/art%3A10.1186%2F1471-2202-14-37/MediaObjects/12868_2012_Article_2893_Fig2_HTML.jpg
         https://media.springernature.com/lw1200/springer-static/image/art%3A10.1186%2F1471-2202-14-37/MediaObjects/12868_2012_Article_2893_Fig3_HTML.jpg
         https://media.springernature.com/lw1200/springer-static/image/art%3A10.1186%2F1471-2202-14-37/MediaObjects/12868_2012_Article_2893_Fig4_HTML.jpg
      isPartOf:
         name:BMC Neuroscience
         issn:
            1471-2202
         volumeNumber:14
         type:
            Periodical
            PublicationVolume
      publisher:
         name:BioMed Central
         logo:
            url:https://www.springernature.com/app-sn/public/images/logo-springernature.png
            type:ImageObject
         type:Organization
      author:
            name:Stanley E Lazic
            affiliation:
                  name:Novartis Institutes for Biomedical Research
                  address:
                     name:In Silico Lead Discovery, Novartis Institutes for Biomedical Research, Switzerland
                     type:PostalAddress
                  type:Organization
            email:[email protected]
            type:Person
            name:Laurent Essioux
            affiliation:
                  name:F. Hoffmann-La Roche
                  address:
                     name:Bioinformatics and Exploratory Data Analysis, F. Hoffmann-La Roche, Basel, Switzerland
                     type:PostalAddress
                  type:Organization
            type:Person
      isAccessibleForFree:1
["Periodical","PublicationVolume"]:
      name:BMC Neuroscience
      issn:
         1471-2202
      volumeNumber:14
Organization:
      name:BioMed Central
      logo:
         url:https://www.springernature.com/app-sn/public/images/logo-springernature.png
         type:ImageObject
      name:Novartis Institutes for Biomedical Research
      address:
         name:In Silico Lead Discovery, Novartis Institutes for Biomedical Research, Switzerland
         type:PostalAddress
      name:F. Hoffmann-La Roche
      address:
         name:Bioinformatics and Exploratory Data Analysis, F. Hoffmann-La Roche, Basel, Switzerland
         type:PostalAddress
ImageObject:
      url:https://www.springernature.com/app-sn/public/images/logo-springernature.png
Person:
      name:Stanley E Lazic
      affiliation:
            name:Novartis Institutes for Biomedical Research
            address:
               name:In Silico Lead Discovery, Novartis Institutes for Biomedical Research, Switzerland
               type:PostalAddress
            type:Organization
      email:[email protected]
      name:Laurent Essioux
      affiliation:
            name:F. Hoffmann-La Roche
            address:
               name:Bioinformatics and Exploratory Data Analysis, F. Hoffmann-La Roche, Basel, Switzerland
               type:PostalAddress
            type:Organization
PostalAddress:
      name:In Silico Lead Discovery, Novartis Institutes for Biomedical Research, Switzerland
      name:Bioinformatics and Exploratory Data Analysis, F. Hoffmann-La Roche, Basel, Switzerland

External Links {🔗}(231)

Analytics and Tracking {📊}

Google Tag Manager

Libraries {📚}

Clipboard.js
Prism.js

CDN Services {📦}

Crossref

4.82s.

LINK . SPRINGER . COM {}