We are analyzing https://link.springer.com/article/10.1186/1471-2288-11-41.

Title:
Quantifying, displaying and accounting for heterogeneity in the meta-analysis of RCTs using standard and generalised Qstatistics | BMC Medical Research Methodology
Description:
Background Clinical researchers have often preferred to use a fixed effects model for the primary interpretation of a meta-analysis. Heterogeneity is usually assessed via the well known Q and I 2 statistics, along with the random effects estimate they imply. In recent years, alternative methods for quantifying heterogeneity have been proposed, that are based on a
Website Age:
28 years and 1 months (reg. 1997-05-29).

Matching Content Categories {📚}

Education
Insurance
Social Networks

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 don't see any clear sign of profit-making.

Not all websites focus on profit; some are designed to educate, connect people, or share useful tools. People create websites for numerous reasons. And this could be one such example. Link.springer.com could be getting rich in stealth mode, or the way it's monetizing isn't detectable.

Keywords {🔍}

metaanalysis, heterogeneity, effects, estimate, article, trials, google, scholar, random, study, fixed, data, effect, estimates, figure, metaanalyses, pubmed, cancer, statistic, chemotherapy, intervals, statistics, reference, clinical, original, methods, model, results, trial, studies, plot, authors, standard, based, journal, confidence, research, variance, dersimonian, distribution, generalised, individual, patient, nsclc, review, method, table, analysis, higgins, size,

Topics {✒️}

van der tweel vinca alkaloid/etoposide agent sarcoma meta-analysis collaboration small-cell lung cancer pooling real-world data meta-analysis inappropriate altogether adult high-grade glioma treatment-effect estimates adjusted article download pdf trials methodology research meta-analytical techniques based henmi-copas method combining random effects meta-analysis random-effects meta-analysis efficient decision-making tool funnel plot asymmetry popular meta-analytic methods privacy choices/manage cookies α-level confidence set full size image advanced ovarian cancer meta-analysis treatment comparison hazard ratio estimates α-level confidence intervals hazard ratio measures authors’ original file random effect smodel random effects model pre-publication history meta-analysis group fixed-effect result research synthesis methods original research process invasive bladder cancer clinical advice influenced full access random effect estimates confidence interval derived random effects models random-effects models fixed effects model added clinical insight conflicting clinical interpretations short cycle chemotherapy related subjects upper reference levels random effects estimation reference interval appears fixed effect estimate random effects estimate

Questions {❓}

Are these worth using?
Huedo-Medina T, Sanchez-Meca J, Marin-Martinez F, Botella J: Assessing Heterogeneity in Meta-Analysis: Q Statistic or I 2 Index?
Standard or Generalised Qstatistic?

Schema {🗺️}

WebPage:
      mainEntity:
         headline:Quantifying, displaying and accounting for heterogeneity in the meta-analysis of RCTs using standard and generalised Qstatistics
         description:Clinical researchers have often preferred to use a fixed effects model for the primary interpretation of a meta-analysis. Heterogeneity is usually assessed via the well known Q and I 2 statistics, along with the random effects estimate they imply. In recent years, alternative methods for quantifying heterogeneity have been proposed, that are based on a 'generalised' Q statistic. We review 18 IPD meta-analyses of RCTs into treatments for cancer, in order to quantify the amount of heterogeneity present and also to discuss practical methods for explaining heterogeneity. Differing results were obtained when the standard Q and I 2 statistics were used to test for the presence of heterogeneity. The two meta-analyses with the largest amount of heterogeneity were investigated further, and on inspection the straightforward application of a random effects model was not deemed appropriate. Compared to the standard Q statistic, the generalised Q statistic provided a more accurate platform for estimating the amount of heterogeneity in the 18 meta-analyses. Explaining heterogeneity via the pre-specification of trial subgroups, graphical diagnostic tools and sensitivity analyses produced a more desirable outcome than an automatic application of the random effects model. Generalised Q statistic methods for quantifying and adjusting for heterogeneity should be incorporated as standard into statistical software. Software is provided to help achieve this aim.
         datePublished:2011-04-07T00:00:00Z
         dateModified:2011-04-07T00:00:00Z
         pageStart:1
         pageEnd:12
         license:http://creativecommons.org/licenses/by/2.0
         sameAs:https://doi.org/10.1186/1471-2288-11-41
         keywords:
            Random Effect Model
            Reference Interval
            Funnel Plot Asymmetry
            Clinical Trial Unit
            Fixed Effect Estimate
            Theory of Medicine/Bioethics
            Statistical Theory and Methods
            Statistics for Life Sciences
            Medicine
            Health Sciences
         image:
            https://media.springernature.com/lw1200/springer-static/image/art%3A10.1186%2F1471-2288-11-41/MediaObjects/12874_2010_Article_564_Fig1_HTML.jpg
            https://media.springernature.com/lw1200/springer-static/image/art%3A10.1186%2F1471-2288-11-41/MediaObjects/12874_2010_Article_564_Fig2_HTML.jpg
            https://media.springernature.com/lw1200/springer-static/image/art%3A10.1186%2F1471-2288-11-41/MediaObjects/12874_2010_Article_564_Fig3_HTML.jpg
            https://media.springernature.com/lw1200/springer-static/image/art%3A10.1186%2F1471-2288-11-41/MediaObjects/12874_2010_Article_564_Fig4_HTML.jpg
            https://media.springernature.com/lw1200/springer-static/image/art%3A10.1186%2F1471-2288-11-41/MediaObjects/12874_2010_Article_564_Fig5_HTML.jpg
            https://media.springernature.com/lw1200/springer-static/image/art%3A10.1186%2F1471-2288-11-41/MediaObjects/12874_2010_Article_564_Fig6_HTML.jpg
         isPartOf:
            name:BMC Medical Research Methodology
            issn:
               1471-2288
            volumeNumber:11
            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:Jack Bowden
               affiliation:
                     name:MRC Clinical Trials Unit
                     address:
                        name:MRC Clinical Trials Unit, London, UK
                        type:PostalAddress
                     type:Organization
                     name:MRC Biostatistics Unit
                     address:
                        name:MRC Biostatistics Unit, Cambridge, UK
                        type:PostalAddress
                     type:Organization
               email:[email protected]
               type:Person
               name:Jayne F Tierney
               affiliation:
                     name:MRC Clinical Trials Unit
                     address:
                        name:MRC Clinical Trials Unit, London, UK
                        type:PostalAddress
                     type:Organization
               type:Person
               name:Andrew J Copas
               affiliation:
                     name:MRC Clinical Trials Unit
                     address:
                        name:MRC Clinical Trials Unit, London, UK
                        type:PostalAddress
                     type:Organization
               type:Person
               name:Sarah Burdett
               affiliation:
                     name:MRC Clinical Trials Unit
                     address:
                        name:MRC Clinical Trials Unit, London, UK
                        type:PostalAddress
                     type:Organization
               type:Person
         isAccessibleForFree:1
         type:ScholarlyArticle
      context:https://schema.org
ScholarlyArticle:
      headline:Quantifying, displaying and accounting for heterogeneity in the meta-analysis of RCTs using standard and generalised Qstatistics
      description:Clinical researchers have often preferred to use a fixed effects model for the primary interpretation of a meta-analysis. Heterogeneity is usually assessed via the well known Q and I 2 statistics, along with the random effects estimate they imply. In recent years, alternative methods for quantifying heterogeneity have been proposed, that are based on a 'generalised' Q statistic. We review 18 IPD meta-analyses of RCTs into treatments for cancer, in order to quantify the amount of heterogeneity present and also to discuss practical methods for explaining heterogeneity. Differing results were obtained when the standard Q and I 2 statistics were used to test for the presence of heterogeneity. The two meta-analyses with the largest amount of heterogeneity were investigated further, and on inspection the straightforward application of a random effects model was not deemed appropriate. Compared to the standard Q statistic, the generalised Q statistic provided a more accurate platform for estimating the amount of heterogeneity in the 18 meta-analyses. Explaining heterogeneity via the pre-specification of trial subgroups, graphical diagnostic tools and sensitivity analyses produced a more desirable outcome than an automatic application of the random effects model. Generalised Q statistic methods for quantifying and adjusting for heterogeneity should be incorporated as standard into statistical software. Software is provided to help achieve this aim.
      datePublished:2011-04-07T00:00:00Z
      dateModified:2011-04-07T00:00:00Z
      pageStart:1
      pageEnd:12
      license:http://creativecommons.org/licenses/by/2.0
      sameAs:https://doi.org/10.1186/1471-2288-11-41
      keywords:
         Random Effect Model
         Reference Interval
         Funnel Plot Asymmetry
         Clinical Trial Unit
         Fixed Effect Estimate
         Theory of Medicine/Bioethics
         Statistical Theory and Methods
         Statistics for Life Sciences
         Medicine
         Health Sciences
      image:
         https://media.springernature.com/lw1200/springer-static/image/art%3A10.1186%2F1471-2288-11-41/MediaObjects/12874_2010_Article_564_Fig1_HTML.jpg
         https://media.springernature.com/lw1200/springer-static/image/art%3A10.1186%2F1471-2288-11-41/MediaObjects/12874_2010_Article_564_Fig2_HTML.jpg
         https://media.springernature.com/lw1200/springer-static/image/art%3A10.1186%2F1471-2288-11-41/MediaObjects/12874_2010_Article_564_Fig3_HTML.jpg
         https://media.springernature.com/lw1200/springer-static/image/art%3A10.1186%2F1471-2288-11-41/MediaObjects/12874_2010_Article_564_Fig4_HTML.jpg
         https://media.springernature.com/lw1200/springer-static/image/art%3A10.1186%2F1471-2288-11-41/MediaObjects/12874_2010_Article_564_Fig5_HTML.jpg
         https://media.springernature.com/lw1200/springer-static/image/art%3A10.1186%2F1471-2288-11-41/MediaObjects/12874_2010_Article_564_Fig6_HTML.jpg
      isPartOf:
         name:BMC Medical Research Methodology
         issn:
            1471-2288
         volumeNumber:11
         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:Jack Bowden
            affiliation:
                  name:MRC Clinical Trials Unit
                  address:
                     name:MRC Clinical Trials Unit, London, UK
                     type:PostalAddress
                  type:Organization
                  name:MRC Biostatistics Unit
                  address:
                     name:MRC Biostatistics Unit, Cambridge, UK
                     type:PostalAddress
                  type:Organization
            email:[email protected]
            type:Person
            name:Jayne F Tierney
            affiliation:
                  name:MRC Clinical Trials Unit
                  address:
                     name:MRC Clinical Trials Unit, London, UK
                     type:PostalAddress
                  type:Organization
            type:Person
            name:Andrew J Copas
            affiliation:
                  name:MRC Clinical Trials Unit
                  address:
                     name:MRC Clinical Trials Unit, London, UK
                     type:PostalAddress
                  type:Organization
            type:Person
            name:Sarah Burdett
            affiliation:
                  name:MRC Clinical Trials Unit
                  address:
                     name:MRC Clinical Trials Unit, London, UK
                     type:PostalAddress
                  type:Organization
            type:Person
      isAccessibleForFree:1
["Periodical","PublicationVolume"]:
      name:BMC Medical Research Methodology
      issn:
         1471-2288
      volumeNumber:11
Organization:
      name:BioMed Central
      logo:
         url:https://www.springernature.com/app-sn/public/images/logo-springernature.png
         type:ImageObject
      name:MRC Clinical Trials Unit
      address:
         name:MRC Clinical Trials Unit, London, UK
         type:PostalAddress
      name:MRC Biostatistics Unit
      address:
         name:MRC Biostatistics Unit, Cambridge, UK
         type:PostalAddress
      name:MRC Clinical Trials Unit
      address:
         name:MRC Clinical Trials Unit, London, UK
         type:PostalAddress
      name:MRC Clinical Trials Unit
      address:
         name:MRC Clinical Trials Unit, London, UK
         type:PostalAddress
      name:MRC Clinical Trials Unit
      address:
         name:MRC Clinical Trials Unit, London, UK
         type:PostalAddress
ImageObject:
      url:https://www.springernature.com/app-sn/public/images/logo-springernature.png
Person:
      name:Jack Bowden
      affiliation:
            name:MRC Clinical Trials Unit
            address:
               name:MRC Clinical Trials Unit, London, UK
               type:PostalAddress
            type:Organization
            name:MRC Biostatistics Unit
            address:
               name:MRC Biostatistics Unit, Cambridge, UK
               type:PostalAddress
            type:Organization
      email:[email protected]
      name:Jayne F Tierney
      affiliation:
            name:MRC Clinical Trials Unit
            address:
               name:MRC Clinical Trials Unit, London, UK
               type:PostalAddress
            type:Organization
      name:Andrew J Copas
      affiliation:
            name:MRC Clinical Trials Unit
            address:
               name:MRC Clinical Trials Unit, London, UK
               type:PostalAddress
            type:Organization
      name:Sarah Burdett
      affiliation:
            name:MRC Clinical Trials Unit
            address:
               name:MRC Clinical Trials Unit, London, UK
               type:PostalAddress
            type:Organization
PostalAddress:
      name:MRC Clinical Trials Unit, London, UK
      name:MRC Biostatistics Unit, Cambridge, UK
      name:MRC Clinical Trials Unit, London, UK
      name:MRC Clinical Trials Unit, London, UK
      name:MRC Clinical Trials Unit, London, UK

External Links {🔗}(153)

Analytics and Tracking {📊}

Google Tag Manager

Libraries {📚}

Clipboard.js
Prism.js

CDN Services {📦}

Crossref

4.58s.

LINK . SPRINGER . COM {}