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The primary rationale for model-based meta-analysis (MBMA) is to improve decision-making by better leveraging prior information from multiple sources. Decision-makers generally attempt to consider such prior information, but it is usually done in a relatively qualitative manner, and each individual decision-maker is usually aware of only a subset of the prior information. MBMA seeks to make the process more quantitative and comprehensive. The process and results of MBMA may be made visible (aka transparent) to the decision-makers. The end result is that the decision-makers are better informed, and they can contribute their knowledge to the modeling process leading to better, more trusted models and model-based inferences.