Description
This is a course is aimed at intervention scientists working in any area—public health, education, criminal justice, and others—interested in learning about an innovative framework for conducting intervention research.

The course will show you how to use the multiphase optimization strategy (MOST) to: streamline interventions by eliminating inactive components; identify the combination of components that offers the greatest effectiveness without exceeding a defined implementation budget; develop interventions for immediate scalability; look inside the “black box” to understand which intervention components work and which do not; and improve interventions programmatically over time. In this course you will relate the MOST framework to your research objectives; learn how MOST differs from the standard approach to intervention development and evaluation; learn how to complete the preparation and optimization phases of MOST; and become familiar with rigorous and highly efficient experimental designs that will enable you to examine the performance of individual intervention components. It will also cover practical issues, such as conducting an optimization trial in a field setting, and strategy for submitting a grant proposal to support intervention optimization.

This course will be held online using a “flipped classroom” combination synchronous/asynchronous approach. To prepare for each synchronous session, learners will be expected to have completed (i) specific modules of the asynchronous Coursera course “Introduction to the Multiphase Optimization Strategy (MOST)” and (ii) an assignment applying what is learned to the learner’s own research. The assignment will be presented briefly and discussed in small breakout groups.

Completing the Coursera course in advance is essential because very little time will be devoted to lectures during the synchronous sessions. Instead, during our time together we will focus on reinforcing what was learned in the Coursera course by: addressing questions that arose during completion of the Coursera modules; discussing the material to relate the ideas to specific research agendas; conducting small group presentations/discussion of the assignments; and reviewing practical issues such as how to write a successful grant proposal related to intervention optimization.

Prerequisites
A PhD, MD, or equivalent degree, and graduate training in applied statistics at least through multiple regression.

Training faculty

• Linda M. Collins, Ph.D., New York University
• Kate Guastaferro, Ph.D., New York University
• J. Nick Dionne-Odom, Ph.D., University of Alabama Birmingham
• Angela F. Pfammatter, Ph.D., University of Tennessee Knoxville
• Jillian C. Strayhorn, Ph.D., New York University (Associate faculty)

Days/times of synchronous sessions
May 22 – 25, 2023, 11am-2 pm EDT each day. Participants will also be assigned to a discussion group that meets either in the morning (9-10:50am) or afternoon (2:30 – 4:30pm). Applicants can indicate their preference for a morning or afternoon group, but we cannot guarantee we can meet your preference.

Learners will need to set aside additional time to complete the Coursera course (which consists of six modules) and the assignments. This can be done at any time before the relevant synchronous portion of the course begins, as indicated on the agenda.

Registration fee: $500.*
There is no additional charge for the Coursera course (an optional certificate of completion can be obtained from Coursera for a fee).

*There is a discount for cadio affiliates. If you’d like to attend the training, but it would cause a financial hardship please complete the application and contact us.

Contact details:
Kate Guastaferro, kate.guastaferro@nyu.edu