Noncompliance, a ubiquitous problem in randomized clinical trials (RCTs), can bias the estimation of treatment effect by the standard intention-to-treat analysis. The complier average causal effect (CACE) measures the effect of an intervention in the latent subpopulation that complies with its assigned treat-ment (the compliers). Though several methods have been developed to estimate CACE in the analysis of a single RCT, methods estimating CACE in meta-analysis of RCTs with noncompliance awaits further development. Here, the authors review the assumptions and estimation of CACE in a single RCT, and propose a frequentist alternative via a generalized linear mixed model to estimate CACE in a meta-analysis. It naturally accounts for the between-study heteroge-neity via random effects. The authors implement the methods in a commonly used software SAS, and describe a case study of a meta-analysis of 10 RCTs evaluating the effect of receiving epidural analgesia in labor on cesarean section, where noncompliance varied dramatically between studies. Furthermore, extensive simulation studies are conducted to evaluate the performance.