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Cancellation Policy: If you cancel your registration at least two weeks before the course is scheduled to begin, you are entitled to a full refund (minus a processing fee of $50).

In the unlikely event that Statistical Horizons LLC must cancel a seminar, we will do our best to inform you as soon as possible of the cancellation. You would then have the option of receiving a full refund of the seminar fee or a credit towards another seminar. In no event shall Statistical Horizons LLC be liable for any incidental or consequential damages that you may incur because of the cancellation.

A 4-Day Livestream Seminar Taught by Nicholas Seewald, Ph.D

In many settings, such as the treatment of mental health or substance use disorders, oncology, schools, and more, there are outstanding scientific questions about how to tailor treatment to an individual’s changing needs. Adaptive interventions (also called dynamic treatment regimens or adaptive treatment strategies) provide guidelines for whether, how, when, or for whom to change the type, dose, or delivery of treatment. Specifically, adaptive interventions are sequences of decision rules that use ongoing patient information to recommend subsequent treatment, whether that treatment be behavioral, educational, pharmacological, or psychosocial. In schools, personnel might iteratively adapt intervention(s) to address the dynamic needs and contexts of students.

Sequential, multiple-assignment, randomized trials (SMARTs) can be used to construct effective adaptive interventions. The key feature of a SMART is that some or all trial participants are randomized multiple times. This sequential randomization allows researchers to address scientific questions at multiple stages of the development of a high-quality adaptive intervention, such as those about how best to initiate an adaptive intervention, how to define subsets of patients who most benefit from certain adaptations, or how best to adapt treatment for those individuals whose first-line intervention was ineffective. SMARTs can be used to build an evidence base for adaptive treatment of disease, strategies for engagement in treatment, or even implementation interventions to improve adoption of evidence-based practices.

In this 4-day workshop, participants will gain detailed knowledge of adaptive interventions and SMARTs, including common design principles and pitfalls, as well as analytic strategies for data from SMARTs. Topics discussed will include common primary, secondary, and exploratory aims in a SMART; power and sample size considerations; primary and secondary data analysis methods; and (time permitting) machine learning approaches to developing more deeply tailored adaptive interventions from SMART data. Throughout, emphasis will be on practical guidance and include case studies and hands-on programming experiences in R.