The opioid epidemic in the US is a public health emergency, exacerbated by the Covid-19 pandemic. Medi- cations for opioid use disorder (MOUD)-injection naltrexone, buprenorphine, and methadone-are the most effective tools for improving outcomes and preventing overdose among persons with OUD, but engagement in MOUD, especially long-term engagement typically required for a successful outcome, is unacceptably low. Long-term engagement rates tend to be even lower in real-world settings-what NIDA has termed the research-to-practice gap. This discrepancy between trial and real-world MOUD effectiveness could be par- tially attributable to differences between clinical trial versus real-world population characteristics (e.g., in terms of psychiatric and substance use comorbidities, previous treatment experience, immigration status, etc.) if treatment effects are modified (increased/decreased) by some of these characteristics that also relate to trial participation. Moreover, without knowing the relative effectiveness of MOUDs for certain real-world target pop- ulations, clinicians, researchers, and policymakers may be tasked with decision-making with biased evidence. Thus, there is a critical need to improve the generalizability of MOUD trials. Failing to meet this need would further ossify the research-to-practice gap, resulting in suboptimal treatment of OUD overall and within key subgroups. We propose to develop design and analytic approaches, what we call a generalizability through- line, to bridge MOUD trial evidence to real-world populations. The objectives of this project are: In Aim 1), to identify and characterize clinically meaningful, interpretable subgroups of persons seeking OUD treatment in US usual-care settings who are not represented or under-represented in MOUD trials based on multiple char- acteristics simultaneously. This will move us beyond existing approaches for assessing representation that have generally been limited to considering one individual-level characteristic at a time (e.g., race/ethnicity). We will apply the approach developed in the first part of Aim 1 to trial data (3 MOUD trials from NIDA CTN) and population data (California and New Jersey Medicaid claims) to characterize under-represented subgroups. In Aim 2), to generalize MOUD effectiveness to state-specific adult Medicaid populations, thereby estimating a realistic treatment goal if treatment retention supports, incentives, and dosing practices were improved to align with those in trials. Existing approaches for predicting generalized effects rely on extrapolation for non- and under-represented subgroups, which can result in biased and/or uninformative estimates. The approach developed in the first part of Aim 2 will make several improvements to limit extrapolation and increase effi- ciency. In Aim 3), to implement the methods developed for Aims 1 and 2 in user-friendly software to facilitate the easy adoption by applied trialists, researchers, and clinicians. Th...