Developing multicomponent interventions using fractional factorial designs
Open accessAbstract
Multicomponent interventions composed of behavioral, delivery, or implementation factors in addition to medications are becoming increasingly common in health sciences. A natural experimental approach to developing and refining such multicomponent interventions is to start with a large number of potential components and screen out the least active ones. Factorial designs can be used efficiently in this endeavor. We address common criticisms and misconceptions regarding the use of factorial designs in these screening studies. We also provide an operationalization of screening studies. As an example, we consider the use of a screening study in the development of a multicomponent smoking cessation intervention. Simulation results are provided to support the discussions.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Factorial designs are often used in MOST's optimization stage to develop more potent interventions.
“factorial designs (along with other approaches) are often used in the optimization stage of MOST to develop more potent interventions [65,67]”
From Introduction to SMART designs for the development of adaptive interventions: with application to weight loss research (2014) · cites this paper as [65]
The citation lists haven’t been fetched yet
References 57 · Cited by 107
- Authors
- Chakraborty, B., Collins, L. M., Strecher, V. J., & Murphy, S. A.
- Affiliations
- University of Michigan (US)
- Pennsylvania State University (US)
- Corresponding author
- Chakraborty, Bibhas
- Venue
- Statistics in Medicine
- Publisher
- Wiley
- Published
- 2009-07-03
- Volume
- 28
- Issue
- 21
- Pages
- 2687-2708
- ISSN
- 0277-6715 (ISSN-L)
- 1097-0258
- PMC
- PMC2746448
- OpenAlex
- W2126394030
- Cited by
- 107
- References
- 57
- Citations by year
- 2026: 4 · 2025: 3 · 2024: 5 · 2023: 6 · 2022: 11 · 2021: 7 · 2020: 10 · 2019: 8 · 2018: 9 · 2017: 5 · 2016: 9 · 2015: 11 · 2014: 6 · 2013: 5 · 2012: 6
- FWCI
- 3.61
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- Locations
- Statistics in Medicine (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Deep Blue (University of Michigan) (submittedVersion)
- Funders
- National Institutes of Health (RO1 MH080015, P50 DA10075, P50 CA101451)
- Topics
- Social Sciences › Decision Sciences › Management Science and Operations Research › Optimal Experimental Design Methods
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
- Health Sciences › Medicine › Physiology › Smoking Behavior and Cessation
- Keywords
- multicomponent intervention, fractional factorial design, factorial design, screening studies, intervention development, smoking cessation interventions, natural experiment
- MeSH terms
- Factor Analysis, Statistical, Follow-Up Studies, Humans, Mass Screening, Research Design, Models, Statistical, Smoking Cessation
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
