Linear mixed model approach to network meta-analysis for continuous outcomes in periodontal research.

Aim Analysing continuous outcomes for network meta-analysis by means of linear mixed models is a great challenge, as it requires statistical software packages to specify special patterns of model error variance and covariance structure. This article demonstrates a non-Bayesian approach to network me...

Descripción completa

Detalles Bibliográficos
Publicado en:Journal of Clinical Periodontology Vol. 42; no. 2; pp. 204 - 213
Autor principal: Tu, Yu-Kang
Formato: equations & formulas meta analysis research systematic review tables/charts Journal Article
Publicado: Wiley-Blackwell Feb2015
Acceso en línea:Ver este registro en EBSCOhost
fields @attributes:
  recordID: 1
pdfLink:
plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=103759330&site=ehost-live
header:
  @attributes:
    shortDbName: ccm
    uiTerm: 103759330
    longDbName: CINAHL Complete
    uiTag: AN
  controlInfo:
    bkinfo:
    dissinfo:
    jinfo:
      jid:
        03036979
        8E3
      jtl: Journal of Clinical Periodontology
      issn: 03036979
      maglogo: Y
    pubinfo:
      dt: Feb2015
      vid: 42
      iid: 2
      pid: 480
      pub: Wiley-Blackwell
      place: Malden, Massachusetts
    artinfo:
      ui:
        103759330
        100988335
        10.1111/jcpe.12362
        NLM25581572
        103759330
      ppf: 204
      ppct: 9
      formats:
      tig:
        atl: Linear mixed model approach to network meta-analysis for continuous outcomes in periodontal research.
      aug:
        au: Tu, Yu-Kang
        affil: Institute of Epidemiology & Preventive Medicine College of Public Health National Taiwan University
      sug:
        subj:
          Guided Tissue Regeneration
          Periodontal Diseases
          Clinical Research
          Human
          Systematic Review
          Meta Analysis
          Randomized Controlled Trials
          Funding Source
          Data Analysis Software
      ab: Aim Analysing continuous outcomes for network meta-analysis by means of linear mixed models is a great challenge, as it requires statistical software packages to specify special patterns of model error variance and covariance structure. This article demonstrates a non-Bayesian approach to network meta-analysis for continuous outcomes in periodontal research with a special focus on the adjustment of data dependency. Data Seventeen studies on guided tissue regeneration were used to illustrate how the proposed linear mixed models for network meta-analysis of continuous outcomes. Methods & Results Arm-based network meta-analysis use treatment arms from each study as the unit of analysis; when patients are randomly assigned to each arm, data are deemed independent and therefore no adjustment is required for multi-arm trials. Trial-based network meta-analysis use treatment contrasts as the unit of analysis, and therefore treatment contrasts within a multi-arm trial are not independent. This data dependency occurs also in split-mouth studies, and adjustments for data dependency are therefore required. Conclusions Arm-based analysis is the preferred approach to network meta-analysis, when all included studies use the parallel group design and some compare more than two treatment arms. When included studies used designs that yield dependent data, the trial-based analysis is the preferred approach.
      pubtype: Academic Journal
      doctype:
        equations & formulas
        meta analysis
        research
        systematic review
        tables/charts
        Journal Article
      ougenre: Article
    language: English
    refInfo:
    holdings:
      @attributes:
        islocal: N