Following a systematic review, multiple meta-analyses are often performed because multiple outcomes are of interest. For example, in meta-analyses of cancer trials the treatment effect on both overall survival and disease-free survival is important. Such multiple outcomes are not independent; e.g. a patient's time to recurrence often occurs shortly before their time of death, and so disease-free and overall survival are positively correlated. Most reviewers ignore this correlation and simply use a 'univariate' meta-analysis of each outcome independently. Alternatively, multivariate meta-analysis methods can jointly synthesise the outcomes and properly account for their correlation. This allows meta-analysis results for, say, outcome A to be informed by evidence from studies reporting outcome A and - crucially - also from studies reporting only correlated outcome B. Compared to univariate meta-analysis, this greater use of available information leads to summary results with improved statistical properties and potentially even different clinical conclusions. This project aims to facilitate the use of multivariate meta-analysis in practice, by showing its clinical impact and overcoming methodology challenges. We will examine how multivariate meta-analyses changes existing clinical inferences within Cochrane Pregnancy and Childbirth Reviews, where correlated outcomes are common. We will derive statistical measures that reveal the impact of utilising correlation and thus 'flag' when multivariate meta-analysis is important. We will then develop and extend methods for estimating the correlation between outcomes, which are needed to apply the multivariate approach; situations involving individual patient data and only summary data will be considered for binary, continuous, and survival outcomes. Finally, we will develop methods for meta-analysing correlated adverse (rare) outcomes, for which within-study distributions other than multivariate normality are needed.