ABSTRACT Medication development for alcohol use disorder (AUD) is a time-consuming and costly process. Unfortunately, no new medications for AUD have been approved in the past two decades, despite significant investments. A typical path to developing a new medication for AUD includes testing in animals, followed by safety testing in humans, followed by randomized clinical trials. Recently, it has been proposed that testing in humans using experimental psychopharmacology paradigms can detect the initial efficacy of a compound under development. As such, the “signal” of medication benefit over placebo is initially identified in animal models, followed by human laboratory testing, and ultimately tested in randomized clinical trials (RCT). In essence, at each phase in testing, scientists are tasked with making “go/no-go” decisions about candidate pharmacotherapies. In this context, approval by the FDA constitutes the final “go” decision and requires compelling efficacy demonstration in RCTs, which is the gold standard. While a host of factors are involved in making “go/no-go” decisions, the paradigms used in animal and human testing to detect an efficacy signal are crucial to the success of medication development. Further, how to evaluate the preclinical and human evidence for a compound in order to decide, is of paramount importance. To date, the question of which models should be used in preclinical studies and human laboratory studies and how the evidence they provide should be evaluated remains highly subjective. Scientists can argue for models they are most familiar with and preliminary data can be presented with a range of plausible interpretation, all of which is inherently subjective. The proposed R21 application seeks to conduct novel meta-analytic models to test the relationship between AUD medication effect sizes obtained in animal models, human laboratory models, and randomized clinical trials (RCTs). These analyses will test the degree to which models used at each stage of medication development for AUD are predictive of clinical outcomes in RCTs, the gold standard for improving healthcare.