daniel almirall

Daniel Almirall

Associate Professor
Institute for Social Research
Department of Statistics
University of Michigan

dalmiral [arroba] umich [punto] edu
(734) 936 3077

[Publications] [Google Scholar] [Curriculum Vitae] [Methodology Center] [Students] [Software] [Mailing Address] [Some Pictures]


Welcome to my homepage. I am a Research Associate Professor in the Survey Research Center of the Institute for Social Research (ISR) and in the Department of Statistics at the University of Michigan. I am also Co-Director of the Data Science for Dynamic Intervention Decision-making Laboratory (also known as d3lab or d-cubed) within the Quantitative Methodology Program at the ISR. Since 2002, I have been affiliated with the Methodology Center at Penn State University. I have a Ph.D. in Statistics from the Department of Statistics at the University of Michigan (class of 2007). Prior to coming to Michigan, from 2007 and 2009, I was a Research Investigator in the Durham VA Center for Health Services Research and Development in Primary Care (HSR&D), and an Assistant Professor in the Department of Biostatistics and Bioinformatics at Duke University.

Academic Interests

Statisticians are toolmakers: I am a statistician and methodologist. I spend almost all of my time at work researching and developing tools that can be used to (learn how best to) improve health, education and well-being. Broadly speaking, the tools I develop or help develop fall into one of two categories: new approaches to data collection and new approaches to data analysis. I develop these tools primarily for use by other researchers (e.g., psychiatrists, psychologists, education/behavioral scientists or other data scientists) who are developing new interventions for improving health, education and well-being. Often, I work closely with these other scientists to directly apply the methods I develop. In addition, I am lucky to be a part of d3lab, a growing community of senior and junior scientists, postdoctoral fellows, and graduate and undergraduate students with whom I collaborate. I also enjoy mentoring the next generation of statisticians and data scientists: my mentees include undergraduate and graduate students in the Department of Statistics, as well as postdoctoral and early career investigators across a wide variety of health and education research areas.

Specific methodological interest: I am particularly 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, or when--and importantly, based on which measures--to alter the intensity, type, or delivery of treatment at critical decision points during intervention. Adaptive interventions are particularly well-suited for the management of chronic diseases, but can be used in any clinical or educational setting in which sequential medical decision making is essential for the welfare of the individual. They hold the promise of enhancing clinical practice by flexibly tailoring treatments or interventions to individuals when they need it most, and in the most appropriate dose, thereby improving the efficacy and effectiveness of treatment. In health settings, adaptive interventions represent one important tool in the practice of "precision medicine". However, adaptive interventions can also be used to adapt interventions at the organizational level, for example, to encourage clinics or schools to adopt an evidence-based intervention. I devote a great portion of my time to addressing methodological issues in the design of sequential multiple assignment randomized trials (SMARTs), and other randomized trial designs, that can be used to optimize or evaluate adaptive interventions.

Specific substantive interests: As a statistician and methodologist, the methods I develop and help develop can be applied across a wide variety of areas. I am particularly interested in their application in the substantive areas of mental health (e.g., autism, depression, anxiety) and substance abuse, especially as related to children and adolescents.

Key Words: dynamic treatment regimes, adaptive treatment strategies, sequential multiple assignment randomized trials, adaptive implementation interventions ,causal inference, propensity score methods, marginal and structural nested mean models, methods for longitudinal data analysis, health services research, mental health, substance abuse, obesity

Selected Publications

For a more complete and updated list of my publications, see my Google Scholar page. The following is only a selection of my published research and I do not update this list frequently:


Slides of my presentations can be found here; if you click on "Last modified", the files will sort by date (eventually, I will organize the files for easier viewing and download).

For workshop slides on the topic of adaptive interventions and sequential multiple assignment randomized trials (SMART), see here though it is likely that over time, the workshop files under the Resources at the d3lab website will be more up to date.





Mailing Address

Daniel Almirall, PhD
2448 Institute for Social Research
426 Thompson Street
University of Michigan
Ann Arbor, Michigan 48104-2321


L to R: Inbal "Billie" Nahum-Shani, Daniel Almirall, Susan A. Murphy in the Fall 2015

Statistical Reinforcement Learning Lab of the Quantitative Methodology Program, Survey Research Center, Institute for Social Research, Fall 2015.

At the UCLA Kasari Autism Research Lab, February 2015.
L to R: Charlotte DiStefano, Wendy Shih, Ya-Chih "Jilly" Chang, Ansel Almirall (son), me, Connie Kasari, Stephanie Shire.

L to R: Olivia Hackworth, Brook Luers, Tim NeCamp in Spring 2018.

Some of the members of our lab in Spring 2018.

First Published: 01/05/2010; Last Revised: 06/25/2018