Although both randomized and nonrandomized study data relevant to a question of treatment efficacy are often available and separately analyzed, these data are rarely formally combined in a single analysis. One possible reason for this is the apparent or feared disagreement of effect estimates across designs, which can be attributed both to differences in estimand definition and to analyses that may produce biased estimators. This article reviews specific models and general frameworks that aim to harmonize analyses from the two designs and combine them via a single analysis that ideally exploits the relative strengths of each design. The development of such methods is still in its infancy, and examples of applications with joint analyses are rare. This area would greatly benefit from more attention from researchers in statistical methods and applications.


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