Statistical methods for combining results of independent studies

The emphasis of the present thesis is on statistical methods for combining results when only published data from the individual studies are available. This is the scenario Glass (1976) had in mind defining the term meta-analysis and this is still the most common situation in research. Individual data from all the studies could clearly improve the findings from a meta-analysis, but in practice it is usually very difficult, if not impossible, to get all the data from the different experiments. The experiments or studies we are interested in are comparative studies, that is, studies in which a hypothesis is tested comparing a new intervention or treatment with a standard intervention or control. The difference or the association between the two counterparts can be modelled using a single parameter, we generally will call effect size in the following. Possible effect sizes are difference of normal means, standardized mean difference, risk difference, or odds ratio. The data situation for the meta-analysis is then that estimates of the effect size of interest are available from each study as well as estimates of the precision of each study-specific effect size estimate