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It is becoming increasingly popular to elicit informative priors on the basisof historical data. Popular existing priors, including the power prior,commensurate prior, and robust meta-analytic prior provide blanket discounting.Thus, if only a subset of participants in the historical data are exchangeablewith the current data, these priors may not be appropriate. In order to combatthis issue, propensity score (PS) approaches have been proposed. However, PSapproaches are only concerned with the covariate distribution, whereasexchangeability is typically assessed with parameters pertaining to theoutcome. In this paper, we introduce the latent exchangeability prior (LEAP),where observations in the historical data are classified into exchangeable andnon-exchangeable groups. The LEAP discounts the historical data by identifyingthe most relevant subjects from the historical data. We compare our proposedapproach against alternative approaches in simulations and present a case studyusing our proposed prior to augment a control arm in a phase 3 clinical trialin plaque psoriasis with an unbalanced randomization scheme.

LEAP: The latent exchangeability prior for borrowing information from historical data