Meta analysis

meta analysis

It is a statistical approach of merging and assimilating evidence from multiple studies investigating similar research question. Meta-analysis is chiefly dichotomized into approaches: fixed effect and random effect method. Under the fixed-effect model we assume that the true effect size for all studies is identical i.e. i.e. each study samples the same population and the only reason the effect size varies between studies is sampling error (error in estimating the effect size). In random-effect meta-analysis, all studies included in meta-analysis are a random part of a much larger population of similar studies i.e. you assume that each study samples a sub-population, instead of the entire population, where effect sizes differ randomly between sub-populations and therefore, under the random-effects model the goal is not to estimate one true effect, but to estimate the mean of a distribution of effects. In random effect meta-analysis, outlier effect of large study is watered down thereby minimizing heterogeneity. Graphical tools included in every met-analysis are funnel and forest plots. In a funnel plot the size of the effect (defined as a measure of the difference between treatment and control) in each study is plotted on the horizontal axis against standard error15 or sample size9 on the vertical axis. If there are no biases, the graph will tend to have a symmetrical funnel shape centered in the average effect of the studies. When negative studies are missing, the graph shows lack of symmetry. Another tool that is very effective to display the level of heterogeneity is the forest plot. In a forest plot, the estimated effect of each study along with a line representing a confidence interval is drawn. When the effects are similar, the confidence intervals overlap, and heterogeneity is low. The forest plot includes a reference line at the point of no effect (e.g., one for relative risks and odds ratios). When some effects lie on opposite sides of the reference line, it means that the studies are contradictory and heterogeneity is high. Meta-analysis can be carried out for prevalence studies, randomized control trials or diagnostic accuracy studies.