Here you can find various examples of SMARTs that are being used to build adaptive interventions that address a range of health problems.

For example, in the SMART weight loss management study, d3lab researchers seek to develop an adaptive intervention that integrates mobile technology in the treatment of obese/overweight adults (R01 DK108678; PIs Spring & Nahum-Shani). At program entry, all individuals are randomized with equal probability (0.5) to one of two first-stage intervention options, either (1) mobile app alone (App) or (2) a mobile app combined with weekly coaching sessions (App+Coaching). Response status is assessed at weeks 2, 4, and 8. As soon as the individual is classified as a non-responder s/he is re-randomized with equal probability (0.5) to one of two second-stage augmentation tactics: either (1) modest augmentation: adding another technology-based intervention component in the form of supportive text messages, or (2) vigorous augmentation: adding supportive text messages combined with a more traditional weight loss intervention component (either coaching or meal replacement) that the individual was not offered initially. As long as the individual is classified as a responder, they continue with the initial intervention and are not re-randomized.

The goal of this SMART is to answer the following questions:

1. The effects of first-stage intervention options: Does offering App alone initially lead to weight loss by month 6 that is noninferior to offering APP + Coaching?

2. The effects of second-stage intervention options: Is it more beneficial in terms of weight loss at 6 months to offer vigorous vs. modest augmentation to those who did not respond to the initial intervention?

3. Moderators: Does the effect of first stage intervention options vary by baseline information (e.g., gender, BMI); does the effect of second-stage intervention options vary by baseline and time varying moderators (socioeconomic status, self-efficacy, extent of self-monitoring).

4. Comparing adaptive interventions: For example, is it better, in terms of weight loss at 6 months, to (a) start with App alone, and then augment modestly for non-responders and continue for responders, or (b) start with App+Coaching, and then augment vigorously for non-responders and continue for responders. This is a comparison between the least (a) and the most (b) intense/costly adaptive interventions in this SMART. There are 4 adaptive interventions that are embedded in the SMART weight loss management study (see Pfammatter et al., 2019).

Here you can find various examples of SMARTs that are being used to build adaptive interventions that address a range of health problems.

References

Ghosh, P., Nahum-Shani, I., Spring, B., & Chakraborty, B. (2020). Noninferiority and equivalence tests in sequential, multiple assignment, randomized trials (SMARTs). Psychological methods, 25(2), 182.

Lavori, P. W., & Dawson, R. (2004). Dynamic treatment regimes: practical design considerations. Clinical trials, 1(1), 9-20.

Murphy, S. A. (2005). An experimental design for the development of adaptive treatment strategies. Statistics in medicine, 24(10), 1455-1481.

Nahum‐Shani, I., Ertefaie, A., Lu, X., Lynch, K. G., McKay, J. R., Oslin, D. W., & Almirall, D. (2017). A SMART data analysis method for constructing adaptive treatment strategies for substance use disorders. Addiction, 112(5), 901-909.

Pfammatter, A. F., Nahum-Shani, I., DeZelar, M., Scanlan, L., McFadden, H. G., Siddique, J., … & Spring, B. (2019). SMART: study protocol for a sequential multiple assignment randomized controlled trial to optimize weight loss management. Contemporary clinical trials, 82, 36-45.

Highlights

Novel Methods for SMARTs in drug-use and HIV.

d3lab members lead the development of new methodologies that broaden the applicability of the SMART in behavioral science. This includes an R01 funded NIH/NIDA (R01 DA 039901 [2015-2020] MPIs: Nahum-Shani, Almirall) to develop methods for analyzing longitudinal data from SMARTs and a competing renewal (R01 DA039901 [2020-2025] MPIs: Nahum-Shani, Almirall) to develop methods for analyzing intensive longitudinal data from SMARTs in drug-use and HIV.

Lu, X., Nahum‐Shani, I., Kasari, C., Lynch, K.G., Oslin, D.W., Pelham, W.E., Fabiano, G. and Almirall, D., (2016). Comparing dynamic treatment regimes using repeated‐measures outcomes: modeling considerations in SMART studies. Statistics in Medicine , 35(10) 1595–1615 .

Nahum-Shani, I., Ertefaie, A., Lu, X., McKay, J.R., Lynch, K.G., Oslin, D., & Almirall, D. (2017). A SMART Data Analysis Method for Constructing Adaptive Treatment Strategies for Substance Use Disorders. Addiction , 112(5), 901-909.

Nahum-Shani, I., Almirall, Yap, J.R., D. McKay, J., Lynch, K., Freiheit, E., & Dziak, J.J. (2020). SMART longitudinal analysis: A tutorial for using repeated outcome measures from SMART studies to compare adaptive interventions. Psychological Methods, 25(1), 1–29.

Seewald, J.N., Kidwell, M.K., Nahum-Shani, I., Wu, T., McKay, R.J., & Almirall, D. (2020). 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, 29(7), 1891-1912.

SMARTs to Inform Adaptive Implementation Strategies.

d3lab members collaborate on the design of SMARTs intended to investigate how best to sequence and adapt strategies for promoting the implementation of evidence-based interventions. We also lead the development of new methods to analyze data from these (clustered) SMARTs.

Fernandez, M.E., Schlechter, C.R., Del Fiol, G., Gibson, B., Kawamoto, K., Siaperas, T., Pruhs, A., Greene, T., Nahum-Shani, I., Schulthies, S., Nelson, M., Bohner, C., Kramer, H., Borbolla, D., Austin, S., Weir, C., Walker, T.W., Lam, C, Y., Wetter, D.W. (2020). QuitSMART Utah: an implementation study protocol for a cluster-randomized, multi-level Sequential Multiple Assignment Randomized Trial to increase Reach and Impact of tobacco cessation treatment in Community Health Centers. Implementation Science, 15(1), 1-13.

Kilbourne, A. M., Almirall, D., Goodrich, D. E., Lai, Z., Abraham, K. M., Nord, K. M., & Bowersox, N. W. (2014). Enhancing outreach for persons with serious mental illness: 12-month results from a cluster randomized trial of an adaptive implementation strategy. Implementation Science, 9(1), 163.

Smith, Shawna N., Daniel Almirall, Katherine Prenovost, Celeste Liebrecht, Julia Kyle, Daniel Eisenberg, Mark S. Bauer, and Amy M. Kilbourne. “Change in Patient Outcomes After Augmenting a Low-level Implementation Strategy in Community Practices That Are Slow to Adopt a Collaborative Chronic Care Model.” Medical Care 57, no. 7 (2019): 503-511.

Quanbeck, A., Almirall, D., Jacobson, N., Brown, R. T., Landeck, J. K., Madden, L., … & Schumacher, N. (2020). The Balanced Opioid Initiative: protocol for a clustered, sequential, multiple-assignment randomized trial to construct an adaptive implementation strategy to improve guideline-concordant opioid prescribing in primary care. Implementation Science, 15, 1-13.

SMARTs to Inform Adaptive Interventions in Education

d3lab members collaborate on designing SMART studies, developing methods, and providing training to support the development of adaptive interventions in education. This includes a training institute funded by the US Department of Education (R324B180003 MPIs: Almirall, Nahum-Shani) and a comprehensive tutorial (Nahum-Shani & Almirall, 2019) on adaptive interventions in education.

Nahum-Shani, I., Almirall, D. (2019) An Introduction to Adaptive Interventions and SMART Designs in Education (NCSER 2020-001). U.S. Department of Education. Washington, DC: National Center for Special Education Research. View Publication here.

Almirall,D., Kasari, C., McCaffrey, D.F., & Nahum-Shani, I. (2018) Developing Optimized Adaptive Interventions in Education. Journal of Research on Educational Effectiveness, 11:1, 27-34.

Roberts. G., Clemens, N., Doabler, C.T., Vaughn, S., Almirall, D., & Nahum-Shani, I. (conditional acceptance). Multi-tiered systems of support, adaptive interventions and SMART designs. Exceptional Children.

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