Podcast: Adaptive Health Interventions and Causal Inference

Methodology MinutesFebruary 24, 2012

In our latest podcast, host Aaron Wagner interviews Daniel Almirall, Faculty Research Fellow at the University of Michigan's Institute for Social Research and Investigator at The Methodology Center. The discussion focuses on sequential, multiple assignment, randomized trials (SMARTs), which allow scientists to develop adaptive interventions.Danny works with Susan Murphy, the creator of SMART, to develop and promote this new methodological tool. Danny's work on causal inference is also discussed.

 

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Podcast Timeline:

00:00 - introduction and Danny's background

02:38 - SMART designs for adaptive interventions

08:31 - SMART pilot studies

13:51 - an application of SMART

19:33 - causal inference

22:39 - publication update

 

Selected Articles Related to This Podcast

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, 1887-1902. PMCID: PMC3399974

Almirall D., Compton S. N., Rynn M. A., Walkup J. T., & Murphy S. A. (In press). SMARTer discontinuation trials: With application to the treatment of anxious youth. Journal of Child and Adolescent Psychopharmacology. [Download Technical Report]

Almirall D., Lizotte, D., & Murphy S. A. (In press). 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).

Almirall, D., McCaffrey, D. F., Ramchand, R., & Murphy, S. A. (2011). Subgroups analysis when treatment and moderators are time-varying. Prevention Science. Advance online publication. doi: 10.1007/s11121-011-0208-7 PMCID: PMC3135740

Almirall D., Ten Have T., & Murphy S. A. (2010). Structural nested mean models for assessing time-varying effect moderation. Biometrics, 66(1):131-139.

Almirall D., Coffman C. J., Yancy W. S., & Murphy S. A. (2010). Maximum likelihood estimation of the structural nested mean model using SAS PROC NLP. In D. Faries, A. Leon, J.M. Haro, & B. Obenchain (Eds.), Analysis of Observational Health-Care Data Using SAS. SAS Publishing.

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