Can I expect reviewers of grant proposals to understand MOST? If not, how should I handle this in the application? There is not enough room to provide a lot of background.


Over the past ten years or so we have detected a marked increase in the number of grant proposal reviewers who are familiar with, and enthusiastic about, MOST, and who understand the basics of optimization trial designs, including the factorial experiment.  However, we believe it is too soon to expect that all reviewers will be familiar with MOST.  Here are a few suggestions:

Emphasize innovation.  If you can say that your project is the first to apply MOST in a particular area, be sure to emphasize this.

Articulate a vision about what is gained by using MOST.  Be sure to share your optimization objective, and remind the reviewer that the optimization trial will help indicate which components work and which do not.

Don’t provide an entire tutorial on MOST.  Instead, directly contrast how you are proposing to conduct the research compared to how you would conduct it if you were using the treatment package approach. Stick to what is needed for the reviewers to understand YOUR project.

Be clear about the three phases of MOST and which of them you have already accomplished, are proposing to do in this grant, and plan to propose in a subsequent grant.  Some people use a figure for this, which when done properly can serve the dual purpose of helping to explain MOST and clarifying the proposal.

Don’t use the expression “factorial experiment” or “factorial optimization trial” in the Specific Aims page.  (For readers of these FAQ outside the US:  National Institutes of Health grant proposals typically start with a one-page Specific Aims section that is a lengthy, highly structured abstract.) This suggestion is controversial; collaborators of ours have been proud of using a factorial experiment, see it as an important strength, and want to wave it around in the Specific Aims.  Our reservation is that there may still be reviewers who do not understand the fundamental logic of a factorial experiment.  Such reviewers may see you proposing a factorial experiment with, say, 32 experimental conditions and N=320, and see it as a 32-arm RCT with 10 participants per condition.  This would probably cause a knee-jerk reaction of “these people are crazy,” after which the reviewer may not take the rest of the proposal seriously.

Instead, on the Specific Aims page you might consider alluding to the use of a “highly efficient” experimental design.  Then, in the Approach section where you can take a few paragraphs to explain it, come out and say you are proposing a factorial experiment, but immediately say something like “THE READER IS ASKED NOT TO VIEW THIS AS A 32-ARM RCT” (yes, in caps, maybe bold too).  Immediately after this, explain how main effects are computed, and briefly explain the efficiency of the factorial design (see Chapter 3 in Collins, 2018).  We suggest including a table that has all the experimental conditions listed.

Share This…

All Frequently Asked Questions

Search FAQs

Search FAQs text
FAQ category

Further Learning

Whether you are looking for additional support as you prepare a grant proposal involving MOST or practical information helpful in managing your optimization trial, this section provides resources for a deeper dive into intervention optimization.

REDCap with Most

The goal of this manual is to show how one might setup a REDCap project to support a research study with multiple conditions, such as factorial experiments common in the Multiphase Optimization Strategy (MOST) framework.

Informal introduction to factorial experimental designs

The purpose of this page is to clarify some concepts, notation, and terminology related to factorial experimental designs, and to compare and contrast factorial experiments to randomized controlled trials (RCTs). A more in-depth introduction can...


Join the CADIO Mailing List

Keep up to date with the latest news, events, online courses, and resources from CADIO.