Res Synth Methods - Multivariate meta-analysis using individual participant data.

Tópicos

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Resumo

When combining results across related studies, a multivariate meta-analysis allows the joint synthesis of correlated effect estimates from multiple outcomes. Joint synthesis can improve efficiency over separate univariate syntheses, may reduce selective outcome reporting biases, and enables joint inferences across the outcomes. A common issue is that within-study correlations needed to fit the multivariate model are unknown from published reports. However, provision of individual participant data (IPD) allows them to be calculated directly. Here, we illustrate how to use IPD to estimate within-study correlations, using a joint linear regression for multiple continuous outcomes and bootstrapping methods for binary, survival and mixed outcomes. In a meta-analysis of 10 hypertension trials, we then show how these methods enable multivariate meta-analysis to address novel clinical questions about continuous, survival and binary outcomes; treatment-covariate interactions; adjusted risk/prognostic factor effects; longitudinal data; prognostic and multiparameter models; and multiple treatment comparisons. Both frequentist and Bayesian approaches are applied, with example software code provided to derive within-study correlations and to fit the models.

Resumo Limpo

combin result across relat studi multivari metaanalysi allow joint synthesi correl effect estim multipl outcom joint synthesi can improv effici separ univari synthes may reduc select outcom report bias enabl joint infer across outcom common issu withinstudi correl need fit multivari model unknown publish report howev provis individu particip data ipd allow calcul direct illustr use ipd estim withinstudi correl use joint linear regress multipl continu outcom bootstrap method binari surviv mix outcom metaanalysi hypertens trial show method enabl multivari metaanalysi address novel clinic question continu surviv binari outcom treatmentcovari interact adjust riskprognost factor effect longitudin data prognost multiparamet model multipl treatment comparison frequentist bayesian approach appli exampl softwar code provid deriv withinstudi correl fit model

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