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.
Highlights
In meta-analysis, multiple summary results are required when there are multiple effects of interest, such as multiple outcomes (Berkey et al, 1995) or multiple time points (Dear, 1994)
Multivariate meta-analysis model (1) was applied using REML, and the summary results show that, compared with control, hypertension treatment improves the odds of having a normal systolic blood pressure (SBP) and diastolic blood pressure (DBP) (Table 2)
Linear and non-linear relationships can be fitted in each study, and a multivariate meta-analysis used to synthesise intercepts and slopes and other terms that allow for non-linearity, whilst accounting for the within-study correlation between all parameters
Summary
We illustrate how to estimate these within-study correlations using IPD and show how their availability enables multivariate meta-analysis to address clinically relevant questions about continuous, binary, survival and mixed outcomes, across wide range of applications. We focus mainly on a two-stage estimation framework (Simmonds et al, 2005; Riley et al, 2010), where the effect estimates and their variances and correlations are obtained for the outcomes in each trial separately and synthesised in a multivariate model. Subsequent sections describe how to use IPD to produce multiple estimates in each study, along with their variances and correlations that allow the multivariate model to be applied.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
