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.
Furthermore, study specific and pooled confidence intervals always were within admissible values, contrary to the original publication, where metan was used. Conclusionīy using metaprop, no studies with 0% or 100% proportions were excluded from the meta-analysis. In the second meta-analysis, the pooled percentage of cured women was 94% (95% CI: 86%-97%). The first meta-analysis showed a pooled HPV-prevalence of 43% (95% CI: 38%-48%). Metaprop was applied on two published meta-analyses: 1) prevalence of HPV-infection in women with a Pap smear showing ASC-US 2) cure rate after treatment for cervical precancer using cold coagulation. It provides appropriate methods for dealing with proportions close to or at the margins where the normal approximation procedures often break down, by use of the binomial distribution to model the within-study variability or by allowing Freeman-Tukey double arcsine transformation to stabilize the variances. Metaprop implements procedures which are specific to binomial data and allows computation of exact binomial and score test-based confidence intervals. It builds further on the existing Stata procedure metan which is typically used to pool effects (risk ratios, odds ratios, differences of risks or means) but which is also used to pool proportions. Metaprop is a statistical program implemented to perform meta-analyses of proportions in Stata.
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Appropriate and accessible statistical software is needed to produce the summary statistic of interest. Meta-analyses have become an essential tool in synthesizing evidence on clinical and epidemiological questions derived from a multitude of similar studies assessing the particular issue.