The aim then is to use approaches from statistics to derive a pooled estimate closest to the unknown common truth based on how this error is perceived.
Existing methods for meta-analysis yield a weighted average from the results of the individual studies, and what differs is the manner in which these weights are allocated and also the manner in which the uncertainty is computed around the point estimate thus generated.
IPD evidence represents raw data as collected by the study centers.
This distinction has raised the need for different meta-analytic methods when evidence synthesis is desired, and has led to the development of one-stage and two-stage methods.
If you are considering doing a systematic review or meta-analysis, this step-by-step guide aims to support you along the way.
It explains the background to these methodologies, what is involved, and how to get started, keep going, and finish!
On the other hand, indirect aggregate data measures the effect of two treatments that were each compared against a similar control group in a meta-analysis.
For example, if treatment A and treatment B were directly compared vs placebo in separate meta-analyses, we can use these two pooled results to get an estimate of the effects of A vs B in an indirect comparison as effect A vs Placebo minus effect B vs Placebo.
The weight that is applied in this process of weighted averaging with a random effects meta-analysis is achieved in two steps: This means that the greater this variability in effect sizes (otherwise known as heterogeneity), the greater the un-weighting and this can reach a point when the random effects meta-analysis result becomes simply the un-weighted average effect size across the studies.
At the other extreme, when all effect sizes are similar (or variability does not exceed sampling error), no REVC is applied and the random effects meta-analysis defaults to simply a fixed effect meta-analysis (only inverse variance weighting).