Meta-analysis is the joint statistical analysis of results from a number of related studies to obtain an overall perspective on an effect or outcome of interest. There are often multiple dimensions to the same outcome and so it is common for outcomes to be determined by at least two measures. Studies typically report multiple estimates of the same effect. Moreover, the trend toward exhaustive decision analysis to assess health technology demands examination of multiple outcomes. There are numerous forms of multivariate effect size data, which can arise in several ways, including as a comparison of one or more treatments or interventions with a control group on at least one outcome, as multiple outcomes per study, or as the measurement of an outcome at several time points. Multivariate methods in meta-analysis account for the dependence between related outcomes and are generally preferred to univariate methods that assess each outcome separately as they typically provide more information about outcomes of interest, by borrowing strength across effects both within and across studies. The chapter begins with a discussion of different sources of multivariate data. The steps involved in any meta-analysis are discussed, followed by a description of common univariate approaches for analysis of multivariate data in meta-analysis. Models in the multivariate meta-analysis framework are then reviewed.