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
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 very frequently (a * means this is a student I mentored):
*Luers, B., Qian. M., Nahum-Shani. I., Kasari. C. and Almirall D. (under review). Longitudinal Mixed Models for Comparing Embedded Dynamic Treatment Regimens in Sequentially Randomized Trials. [arXiv version].
*Seewald, N., Kidwell, K., Nahum-Shani, I., Wu, T., McKay, J., and Almirall D. (2019). Sample size considerations for comparing dynamic treatment regimens in a sequential multiple-assignment randomized trial with a continuous longitudinal outcome.Statistical Methods in Medical Research [arXiv version].
Almirall D., Nahum-Shani, I., Wang, L., Kasari, C. (2018). Experimental Designs for Research on Adaptive Interventions: Singly- and Sequentially-Randomized Trials.
*Hall, K., Nahum-Shani, I., August, G., Patrick, M., Murphy, S.A., Almirall D. (2018). Adaptive Prevention Designs in Substance Use Prevention.
Boruvka, A., Almirall D., Murphy, S.A. (2017). Assessing Time-Varying Causal Effect Moderation in Mobile Health: Modeling and Estimation Considerations for Intensive Longitudinal Intervention Data. [arXiv version] Journal of the American Statistical Association.
*NeCamp, T., Kilbourne, A. Almirall D. (2017). Cluster-level adaptive interventions and sequential, multiple assignment, randomized trials: Estimation and sample size considerations.Statistical Methods in Medical Research
Almirall D., Chronis-Tuscano, A., (2016). Adaptive interventions in Child and Adolescent Mental Health.Journal of Clinical Child and Adolescent Psychology.
*Hwanwoo, K., Ionides, E., Almirall D. (2016). A Sample Size Calculator for SMART Pilot Studies.Society for Industrial and Applied Mathematics (SIAM): Undergraduate Research Online (SIURO) Journal.
*Lu, X., Nahum-Shani, I., Kasari, C., Lynch, K.G., Oslin, D.W., Pelham, W.E., Fabiano, G., Almirall D. (2015). Comparing dynamic treatment regimes using repeated-measures outcomes: modeling considerations in SMART studiesStatistics in Medicine. [Abstract] [Technical Report of an older version of the manuscript is available from The Methodology Center at Penn State University]
Chronis-Tuscano, A., Wang, C.H., Strickland, J., Almirall D., Stein, M.A. (2016). Moving Toward Personalized Treatment of Mothers with ADHD and Their At-Risk Children: A SMART Pilot.Journal of Clinical Child and Adolescent Psychology.
Almirall D., DiStefano, C., Chang, Y., Shire, S., Lu, X., Nahum-Shani, I., Kasari, C. (2016). Adaptive interventions and longitudinal outcomes in minimally verbal children with ASD: Role of speech-generating devices.Journal of Clinical Child and Adolescent Psychology.
This manuscript was recognized as a "top 20 article" in Autism in 2016 by the Interagency Autism Coordinating Committee; see here.
Gunlicks-Stoessel, M., Mufson, L., Westervelt, A., Almirall D., Murphy S.A. (2014). A Pilot SMART for Developing an Adaptive Treatment Strategy for Adolescent Depression.Journal of Clinical Child and Adolescent Psychology.
Kilbourne, A.M., Almirall, D., Eisenberg, D., Waxmonsky, J., Goodrich, D.E., Fortney, J.C., Kirchner, J.E., Solberg, L.I., Main, D., Bauer, M.S., Kyle, J., Murphy, S.A., Nord, K.M., Thomas, M.R. (2014). Protocol: Adaptive Implementation of Effective Programs Trial (ADEPT): cluster randomized SMART trial comparing a standard versus enhanced implementation strategy to improve outcomes of a mood disorders program.Implementation Science, 9,132. DOI: 10.1186/s13012-014-0132-x.
Almirall D., Nahum-Shani, I., Sherwood, N.E., Murphy S.A. (2014). Introduction to SMART Designs for the Development of Adaptive Interventions: With Application to Weight Loss Research.Translational Behavioral Medicine, 4:260-274. DOI: 10.1007/s13142-014-0265-0 [Technical Report of an older, longer, and not-as-polished version of this manuscript is available from The Methodology Center at Penn State University]
Kasari, C., Kaiser, A., Goods, K., Nietfeld, J., Mathy, P., Landa, R., Murphy, S.A., Almirall D (2014). Communication Interventions for Minimally Verbal Children with Autism: Sequential Multiple Assignment Randomized Trial.Journal of the American Academy of Child and Adolescent Psychiatry. DOI:10.1016/j.jaac.2014.01.019
This manuscript was discussed by Dr. Helen Tager-Flushberg of Boston University; see here.
Almirall D., McCaffrey, D.F., Griffin B.A., Ramchand R., Yuen R., Murphy S.A. (2013). Time-varying effect moderation using the structural nested mean model: estimation using inverse-weighted regression-with-residuals.Statistics in Medicine. [Technical Report of an older version of the manuscript is available from The Methodology Center at Penn State University]
McCaffrey, D.F., Griffin, B.A., Almirall D., Slaughter, M.E., Ramchand, R., Burgette, L.F. (2013). A Tutorial on Propensity Score Estimation for Multiple Treatments using Generalized Boosted Models.Statistics in Medicine. DOI: 10.1002/sim.5753
Almirall D., Compton S.N., Rynn M.A., Walkup J.T., Murphy S.A. (2012). SMARTer Discontinuation Trial Designs for Developing an Adaptive Treatment Strategy.Journal of Child and Adolescent Psychopharmacology 22(5):364-74. DOI: 10.1089/cap.2011.0073. PMID: 23083023. PMCID: 3482379. [Technical Report of an older version of the manuscript is available from The Methodology Center at Penn State University]
Nahum-Shani I., Qian M., Almirall D., Pelham W.E., Gnagy B., Fabiano G., Waxmonsky J., Yu J., Murphy S.A. (2012). Experimental Design and Primary Data Analysis Methods for Comparing Adaptive Interventions.Psychological Methods. [Technical Report of an older version of the manuscript is available from The Methodology Center at Penn State University]
Nahum-Shani I., Qian M., Almirall D., Pelham W.E., Gnagy B., Fabiano G., Waxmonsky J., Yu J., Murphy S.A. (2012). Q-Learning: A Data Analysis Method for Comparing Adaptive Interventions.Psychological Methods. [Technical Report of an older version of the manuscript is available from The Methodology Center at Penn State University]
Almirall D., Lizotte, D., Murphy S.A. (2012). SMART Design Issues and the Consideration of Opposing Outcomes, a Discussion of Evaluation of Viable Dynamic Treatment Regimes in a Sequentially Randomized Trial of Advanced Prostate Cancer by Wang, Rotnitzky, Lin, Millikan, and Thall. Journal of the American Statistical Association (Case Studies and Applications) 107(498):509-12.
Almirall D., Compton S.N., Gunlicks-Stoessel M., Duan N., Murphy S.A. (2012). Designing a Pilot Sequential Multiple Assignment Randomized Trial for Developing an Adaptive Treatment Strategy.Statistics in Medicine 31(17):1887-902. DOI: 10.1002/sim.4512. PMID: 22438190. PMCID: 3399974. [Technical Report of an older version of the manuscript is available from The Methodology Center at Penn State University]
Almirall D., McCaffrey, D.F., Ramchand R., Murphy S.A. (2013). Subgroups Analysis when Treatment and Moderators are Time-varying.Prevention Science, 14(2):169-78.
Almirall D., Ten Have T., Murphy S.A. (2010). Structural Nested Mean Models for Assessing Time-Varying Effect Moderation.Biometrics, 66(1):131-139.
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.