title: Multivariate meta-analysis using individual participant data.
abstract: 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.
authors.affiliation: Research Institute of Primary Care and Health Sciences, Keele University, Staffordshire, ST5 5BG, UK.
authors.forename: R D
authors.initials: RD
authors.lastname: Riley
authors.affiliation: School of Health and Population Sciences, Public Health Building, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK.
authors.forename: M J
authors.initials: MJ
authors.lastname: Price
authors.affiliation: MRC Biostatistics Unit, Cambridge, UK.
authors.forename: D
authors.initials: D
authors.lastname: Jackson
authors.affiliation: School of Mathematics, Watson Building, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK.
authors.forename: M
authors.initials: M
authors.lastname: Wardle
authors.affiliation: UMR5558, CNRS and Lyon 1 Claude Bernard University, Lyon, France.
authors.forename: F
authors.initials: F
authors.lastname: Gueyffier
authors.affiliation: Centre for Epidemiological Studies and Clinical Trials, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Ruijin 2nd Road 197, Shanghai, 200025, China.
authors.forename: J
authors.initials: J
authors.lastname: Wang
authors.affiliation: Research Unit Hypertension and Cardiovascular Epidemiology, KU Leuven Department of Cardiovascular Sciences, University of Leuven, Leuven, Belgium.|Department of Epidemiology, Maastricht University, Maastricht, Netherlands.
authors.forename: J A
authors.initials: JA
authors.lastname: Staessen
authors.affiliation: MRC Biostatistics Unit, Cambridge, UK.
authors.forename: I R
authors.initials: IR
authors.lastname: White
mesh_terms: D001499:Bayes Theorem; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D006801:Humans; D015201:Meta-Analysis as Topic; D015233:Models, Statistical; D015999:Multivariate Analysis; D017063:Outcome Assessment, Health Care / Q000379:methods*; D012107:Research Design; D012984:Software
keywords: bivariate meta-analysis; correlation; individual participant data (IPD); individual patient data; multiple outcomes; multivariate meta-analysis