I’ve already collected a bunch of data. Can I use MOST to analyze this existing data set?
No. MOST is not a data analysis technique. It requires carrying out experiments.
However, secondary analyses of data, for example data from previously conducted RCTs, can be very informative in planning how you will use MOST to optimize and evaluate a behavioral intervention. For example, such analyses can provide important leads for intervention components to be tested.
MOST seems like a great idea in theory, but I do not see how it can be conducted without massive resources. It just does not seem practical.
By definition, MOST works using the resources at hand, so it is meant to be very practical. This is not to say that MOST is suitable for every situation. But before concluding that it is not suitable for your situation, it would be good to take careful stock of the resources you have at your disposal for intervention development and evaluation, and think creatively about this question: How can these resources be leveraged to move intervention science forward fastest and best? See "What is the resource management principle?".
MOST is fundable. It is currently being implemented in projects funded by the National Cancer Institute; the National Institute on Drug Abuse; the National Institute of Diabetes, Digestive, and Kidney Diseases; and the National Heart, Lung, and Blood Institute.
How can a complete cycle of MOST (that is, all three phases) be conducted within the five-year limit on NIH grants, and it is necessary to do so?
Whether you can conduct a cycle of MOST within five years is hugely dependent on the area in which you are working. There are several factors that influence this:
- How far along the field is. In some cases, there is a well-accepted theoretical model in place, and the intervention components have already been selected and pilot tested when a grant proposal is written. This means that the project can start by launching into the optimization phase. In other cases, the theoretical model must be developed before anything else can be done.
- The lag time between when the intervention is implemented and when it makes sense to assess the primary outcome. In areas like smoking cessation, the outcome can be assessed soon after the intervention is over. In areas like weight loss, it may be necessary to wait longer. (In some areas there can be a lag of years and a somewhat different approach may be required; see "What should I use as an outcome variable for the component selection experiment?")
- The rate at which subjects can be obtained. When a universal intervention is being developed for first-year college students, it is often possible to work with a cohort of all first-year students at a university. By contrast, in clinic-based interventions it is usually necessary to wait for subjects to "trickle in."
We have been successful in obtaining funding for studies that use MOST going only up to the optimization phase. For example, in the study described in Baker et al. (2011) and Collins et al. (2011) we successfully proposed conducting MOST up to and including the Optimization Phase, indicating that we would propose the RCT in a subsequent application. (The subsequent application has now been funded.) However, in some other applications, we have been criticized for not proposing an RCT of the optimized intervention.
Baker, T. B., Mermelstein, R. J., Collins, L. M., Piper, M. E., Jorenby, D. E., Smith, S. S., Schlam, T. R. Cook, J. W., & Fiore, M. C. (2011). New methods for tobacco dependence treatment research. Annals of Behavioral Medicine, 41, 192-207. PMCID: PMC3073306 View abstract
Collins, L. M., Baker, T. B., Mermelstein, R. J., Piper, M. E., Jorenby, D. E., Smith, S. S., Schlam, T. R., Cook, J. W., & Fiore, M. C. (2011). The multiphase optimization strategy for engineering effective tobacco use interventions. Annals of Behavioral Medicine, 41, 208-226. PMCID: PMC3053423 View abstract
You may be interested in resources for writing a grant proposal involving MOST.