Empirical comparison of univariate and multivariate meta-analysis in Cochrane Pregnancy and Childbirth reviews with multiple binary outcomes

Abstract

Background: Multivariate meta-analysis (MVMA) jointly synthesise effects for multiple correlated outcomes. The MVMA model is potentially more difficult and time-consuming to apply than univariate models, so if its use makes little difference to parameter estimates it could be argued that it is redundant.

Methods: We assessed the applicability and impact of MVMA in Cochrane Pregnancy and Childbirth (CPCB) systematic reviews. We applied MVMA to CPCB reviews published between 2011 to 2013 with two or more binary outcomes with at least three studies, and compared findings with results of univariate meta-analyses. Univariate random effects meta-analysis models were fitted using restricted maximum likelihood estimation (REML).

Results: 80 CPCB reviews were published. MVMA could not be applied in 70 of these reviews. MVMA was not feasible in 3 of the remaining 10 reviews because the appropriate models failed to converge. Estimates from MVMA agreed with those of univariate analyses in most of the other 7 reviews. Statistical significance changed in 2 reviews: in 1 this was due to a very small change in p-value; in the other, the MVMA result for one outcome suggested previous univariate results may be vulnerable to small study effects and that the certainty of clinical conclusions needs consideration.

Conclusions: MVMA methods can be applied only in a minority of reviews of interventions in pregnancy and childbirth, and can be difficult to apply due to missing correlations or lack of convergence. Nevertheless, clinical and/or statistical conclusions from MVMA may occasionally differ from those from univariate analyses.