Res Synth Methods - The impact of multiple endpoint dependency on Q and I(2) in meta-analysis.

Tópicos

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Resumo

A common assumption in meta-analysis is that effect sizes are independent. When correlated effect sizes are analyzed using traditional univariate techniques, this assumption is violated. This research assesses the impact of dependence arising from treatment-control studies with multiple endpoints on homogeneity measures Q and I(2) in scenarios using the unbiased standardized-mean-difference effect size. Univariate and multivariate meta-analysis methods are examined. Conditions included different overall outcome effects, study sample sizes, numbers of studies, between-outcomes correlations, dependency structures, and ways of computing the correlation. The univariate approach used typical fixed-effects analyses whereas the multivariate approach used generalized least-squares (GLS) estimates of a fixed-effects model, weighted by the inverse variance-covariance matrix. Increased dependence among effect sizes led to increased Type I error rates from univariate models. When effect sizes were strongly dependent, error rates were drastically higher than nominal levels regardless of study sample size and number of studies. In contrast, using GLS estimation to account for multiple-endpoint dependency maintained error rates within nominal levels. Conversely, mean I(2) values were not greatly affected by increased amounts of dependency. Last, we point out that the between-outcomes correlation should be estimated as a pooled within-groups correlation rather than using a full-sample estimator that does not consider treatment/control group membership.

Resumo Limpo

common assumpt metaanalysi effect size independ correl effect size analyz use tradit univari techniqu assumpt violat research assess impact depend aris treatmentcontrol studi multipl endpoint homogen measur q scenario use unbias standardizedmeandiffer effect size univari multivari metaanalysi method examin condit includ differ overal outcom effect studi sampl size number studi betweenoutcom correl depend structur way comput correl univari approach use typic fixedeffect analys wherea multivari approach use general leastsquar gls estim fixedeffect model weight invers variancecovari matrix increas depend among effect size led increas type error rate univari model effect size strong depend error rate drastic higher nomin level regardless studi sampl size number studi contrast use gls estim account multipleendpoint depend maintain error rate within nomin level convers mean valu great affect increas amount depend last point betweenoutcom correl estim pool withingroup correl rather use fullsampl estim consid treatmentcontrol group membership

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