Some of the reasons why this approach is popular include lower level of required statistical expertise, faster computations and availability of software to carry out the analysis. Some of the common transformations include the logit and the arcsine.

The most popular framework uses approximation to the normal distribution by use of transformations and is known as the approximate likelihood approach. There are three frameworks in modeling of binomial data. The variance parameter describes the heterogeneity among the studies and in the case where the variance is zero, this model simply reduces to the fixed-effects model. Each study estimates a different parameter, and the pooled estimate describes the mean of the distribution of the estimated parameters. In the random-effects model, the observed difference between the proportions and the mean cannot be entirely attributed to sampling error and other factors such as differences in study population, study designs, etc. In the fixed-effects model, it is assumed that the parameter of interest is identical across studies and the difference between the observed proportion and the mean is only due to sampling error. A meta-analyst has a choice between the fixed- and random-effects model.

There are three important aspects in meta-analysis: a) the analysis framework, b) the model and c) the choice of the method to estimate the heterogeneity parameter. Examples of statistics of interest include association measures such as risk difference, risk ratio, odds ratio, difference in means, or simply one-dimensional binomial or continuous measures such as proportions or means. Different meta-analysis procedures exist depending on the statistic to be reported.

Meta-analyses combine information from multiple studies in order to derive an average estimate.

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