The concept of meta-analysis of economic evaluation (MAEE) studies was presented in a meeting of the Immunization and Vaccine-related Implementation Research Advisory Committee (IVIR-AC) held on March 2021. The IVIR-AC considered several examples of MAEEs and commented on several methodological issues. Heterogeneity observed in MAEEs is a major methodological challenge. Two-thirds of recently published MAEEs showed evidence of a substantial level of heterogeneity. However, most analyses provide no explanations of the potential sources of heterogeneity. In a recent meta-analysis, we demonstrated that variation in the willingness-to-pay (WTP) threshold across individual studies might be a major source of heterogeneity. We proposed a strategy to apply a uniform WTP threshold across studies, which has been found to be successful in mitigating heterogeneity. There are other sources of heterogeneity while performing MAEE, including study characteristics (such as setting, perspective, and funding source) and methodological characteristics (such as time horizon, data source, model type, input parameters, and model assumptions), which may require different strategies. There is a critical need to explore sources of heterogeneity and develop a standardized approach to handle it to improve the efficiency and acceptability of future MAEEs. In our present approach, we perform MAEE by pooling the incremental net benefit (INB) of each study in a random-effects model using the DerSimonian and Laird method in which studies are weighted according to the variance of the INB since the variance is frequently used in traditional meta-analyses of empirical studies with meaningful sample sizes. Therefore, studies with higher levels of reported uncertainty were given less weight in a meta-analysis. Experts suggested that since these uncertainty intervals depend on how thoroughly the modelers have investigated uncertainty (e.g., what parameters they included in a probabilistic sensitivity analysis, and what sources of uncertainty they incorporated into the uncertainty distributions of each parameter); therefore, larger intervals may reflect relatively better-quality studies that, in some cases, should be given more weight rather than less. The IVIR-AC suggested developing a grid of criteria for assessing study quality, especially focusing on the quality of uncertainty assessment in an economic evaluation as well as the quality of the modelling approach, to enable quantitative synthesis weighted by study quality rather than the traditional approach for empirical studies. An attempt should be made to adopt the methodology recommended by experts to enable MAEE weighted by study quality rather than the traditional approach to recognize how findings differ in both ways.