Treatment effects will differ from one study to another evaluating similar therapies, both because of random variation between individual patients and owing to true differences that exist because of other differences, including inclusion criteria and temporal trends. The sources of variability have many levels; one level involves the random differences between individual patients, and another level involves the systematic differences that exist between studies. This multilevel or hierarchical information occurs in many research settings, such as in cluster-randomized trials and meta-analyses.1,2 Sources of variation can be better understood and quantified if treatment effect estimates from each individual study are examined in relation to the totality of information available in all the studies.
McGlothlin AE, Viele K. Bayesian Hierarchical Models. JAMA. 2018;320(22):2365–2366. doi:10.1001/jama.2018.17977
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