One important problem in meta-analysis is heterogeneity, the result of bias. When inconsistency occurs, traditional work in meta-analysis is employing a random effect model based on inverse variance method to combine the results. Such a method used the moment-based estimator τ2 measuring the deviation from true value across studies to obtain a conservative result. It however failed to estimate the influence on each study due to bias and this method may at risk of underestimate the standard error which then may leads to biased summarized estimator. Accordingly, Doi proposed a new weighting procedure, QE method, hopefully be a good solution. In this article, we will introduce the QE method with details on the methodology and software, and then make a comparison between QE and random effect model of the results.