Research Projects
Harnessing Big Data and Machine Learning to Learn Optimal, Individualized Dynamic Treatment Rules to Prevent Opioid Use Disorder Relapse
Sean Luo, Psychiatry
Min Qian, Biostatistics
Kara Rudolph, Epidemiology
Pharmacologic treatment of opioid use disorder (OUD) is complicated by the likely absence of a one-size-fits-all best approach; rather, “optimal” dose and dose adjustment are hypothesized to depend on person-level factors, including factors that change over time, reflecting how well the individual is responding to treatment. This team will use harmonized data from multiple existing clinical trials with natural variability in OUD medication dose adjustments over time to 1) learn optimal dosing strategies, and 2) estimate the extent to which such optimal dosing strategies could reduce risk of treatment drop-out and relapse.