Meta-analysis and Random Effect Analysis
Heterogeneity is probably the largest pitfall of meta-analyses. A random effect analytic model assumes, that heterogeneity is due to some unexpected subgroup effect rather than a residual effect, and uses a separate random variable for the purpose. Examples of fixed and random effect analyses are given both from binary and continuous outcome meta-analysis data. Within a single study heterogeneity may very well be residual, but between the overall effects of studies within a meta-analysis this is virtually never so, and it is virtually always caused by some subgroup effect. Therefore, fixed effect heterogeneity tests are slightly inappropriate. However, random effect heterogeneity tests lack power. Novel methodologies are being developed, and will be reviewed in this and many subsequent chapters of this edition.
KeywordsRandom Effect Random Effect Model Fixed Effect Model Effect Test Pool Odds Ratio
- More background, theoretical and mathematical information of meta-analyses is given in Statistics applied to clinical studies 5th edition, Chaps. 32–34 and 48, Springer Heidelberg Germany, 2012, from the same authors.Google Scholar