Daniel Almirall, Ph.D.
Ph.D., University of Michigan & Institute for Social Research
M.S., University of Michigan
B.S., University of Florida
I am a data science methodologist who works at the intersection of behavioral interventions science and statistical science, with particular interest in applications in the areas of substance use, mental health (depression, anxiety, ADHD), especially as related to children and adolescents, and autism. On any given day, I am wearing one of three "data science hats": 1) designing novel approaches to data collection that maximize our ability to answer critical questions in the development of high-quality behavioral interventions; 2) extending or developing new data collection or analysis methods; or 3) working with behavioral scientists to apply these methods. I am very fortunate to have the opportunity to lead a career that blends statistics, mathematics, and health to improve the lives of others.
My current methodological research interests lie in the broad area of causal inference. I am particularly interested in methods for causal inference using longitudinal data sets in which treatments, covariates, and outcomes are all time-varying. I am also interested in developing statistical methods that can be used to form adaptive interventions, sometimes known as dynamic treatment regimes. An adaptive intervention is a sequence of individually tailored decisions rules that specify whether, how, and when to alter the intensity, type, or delivery of treatment at critical decision points in the medical care process. Adaptive interventions are particularly well-suited for the management of chronic diseases, but can be used in any clinical setting in which sequential medical decision making is essential for the welfare of the patient. They hold the promise of enhancing clinical practice by flexibly tailoring treatments to patients when they need it most, and in the most appropriate dose, thereby improving the efficacy and effectiveness of treatment. In addition to developing new statistical methodologies, I devote a portion of my research to the design of sequential multiple assignment randomized trials (SMARTs). SMARTs are randomized trial designs that give rise to high-quality data that can be used to develop and optimize adaptive interventions.
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