Search

Meta-analysis is a quantitative method for summarizing single studies. In a meta-analysis, one tries to quantify the treatment effect, assigning more weight to large studies than to small studies. A much used method for weighting is the inverse variance method. If all studies have measured the results in the same way, the results can be used directly in the meta-analysis, but if the same outcome is measured in different ways across different studies, one has to use a standardized effect size where results are converted to a common scale. If it is believed that the effect is consistent across various populations and settings, one can employ a fixed-effect model. If systematic differences in effect can be expected, a random-effects model is used. Meta-analyses are often depicted as forest plots. Each line represents one study where the effect estimate is marked as a point on a line, with each end of the line representing the confidence interval around it. The meta-analysis is shown as a diamond where the width illustrates the uncertainty around the estimate. If all study results point in the same direction, the meta-analysis is considered "homogeneous". But if the studies vary in their effect size and direction, the findings are "heterogeneous". The strength of meta-analysis is that it can be used to summarize a large body of information in one number. This is also its limitation. One number cannot describe the variation that exists across different studies. [ABSTRACT FROM AUTHOR]