Ryan Cook, Ph.D., is an epidemiologist with extensive experience studying the intersection of substance use, HIV and mental health. He has served as co-investigator and statistician for numerous domestic and international epidemiologic studies and clinical and community trials, and has co-authored more than 80 scientific publications. He has led research projects addressing barriers to initiation of medication for opioid use disorder, treatment of opioid use disorder among people living with HIV, prevention of HIV transmission within heterosexual couples and men who have sex with men, the effects of HIV and substance use on the microbiome, and retention in clinical trials for substance use treatment. Many of these studies have concentrated on the intersecting effects of multiple mental health conditions including polysubstance use, HIV, depression, anxiety and cognitive impairment.

Dr. Cook has expertise in epidemiologic and biostatistical methods, and has been formally trained in causal inference, longitudinal/multilevel data analysis, Bayesian statistics, survival analysis and design and analysis of clinical trials, among other statistical methods. He loves learning new analytic methods, doing data analysis and innovatively applying tools across disciplines in order to solve complex problems in research.

His current research, funded by a NIDA K01, applies innovative data science and causal inference techniques to generalize addiction research studies to “real world” patients with substance use disorders. Specifically, he is interested in identifying optimal treatment strategies for people with co-occurring stimulant and opioid use disorders. Co-occurring use of opioids and stimulants is a rapidly growing, critical public health challenge, resulting in alarming numbers of overdoses, hospitalizations and deaths in the past five years. Either by design or incidentally, people with co-occurring psychiatric disorders, including multiple substance use disorders, have been historically underrepresented in treatment clinical trials. Consequently, the effectiveness of existing treatments is not well established in these populations.

Dr. Cook’s research combines data science methods with a novel data-fusion technique, transportability analysis, to address this critical gap in knowledge about treatments for complex, vulnerable patients who are poorly represented in efficacy evaluations.