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In network meta-analyses that synthesize direct and indirect comparison evidence concerning multiple treatments, multivariate random effects models have been routinely used for addressing between-studies heterogeneities. Although their standard inference methods depend on large sample approximations (eg, restricted maximum likelihood estimation) for the number of trials synthesized, the numbers of trials are often moderate or small. In these situations, standard estimators cannot be expected to behave in accordance with asymptotic theory; in particular, confidence intervals cannot be assumed to exhibit their nominal coverage probabilities (also, the type I error probabilities of the corresponding tests cannot be retained). The invalidity issue may seriously influence the overall conclusions of network meta-analyses. In this article, we develop several improved inference methods for network meta-analyses to resolve these problems. We first introduce 2 efficient likelihood-based inference methods, the likelihood ratio test-based and efficient score test-based methods, in a general framework of network meta-analysis. Then, to improve the small-sample inferences, we developed improved higher-order asymptotic methods using Bartlett-type corrections and bootstrap adjustment methods. The proposed methods adopt Monte Carlo approaches using parametric bootstraps to effectively circumvent complicated analytical calculations of case-by-case analyses and to permit flexible application to various statistical models network meta-analyses. These methods can also be straightforwardly applied to multivariate meta-regression analyses and to tests for the evaluation of inconsistency. In numerical evaluations via simulations, the proposed methods generally performed well compared with the ordinary restricted maximum likelihood-based inference method. Applications to 2 network meta-analysis datasets are provided. [ABSTRACT FROM AUTHOR]