The DOI matches its Crossref record; 27 of the 27 references it cites were found in a registry or an index.
Factorial Experiments
Open accessAbstract
Background An understanding of the individual and combined effects of a set of intervention components is important for moving the science of preventive medicine interventions forward. This understanding can often be achieved in an efficient and economical way via a factorial experiment, in which two or more independent variables are manipulated. The factorial experiment is a complement to the RCT; the two designs address different research questions.Purpose To offer an introduction to factorial experiments aimed at investigators trained primarily in the RCT.Methods The factorial experiment is compared and contrasted with other experimental designs used commonly in intervention science to highlight where each is most efficient and appropriate.Results Several points are made: factorial experiments make very efficient use of experimental subjects when the data are properly analyzed; a factorial experiment can have excellent statistical power even if it has relatively few subjects per experimental condition; and when conducting research to select components for inclusion in a multicomponent intervention, interactions should be studied rather than avoided.Conclusions Investigators in preventive medicine and related areas should begin considering factorial experiments alongside other approaches. Experimental designs should be chosen from a resource management perspective, which states that the best experimental design is the one that provides the greatest scientific benefit without exceeding available resources.
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“Indeed, RCTs were originally developed to test single-component interventions (eg, drugs), but psychological interventions usually consist of multiple treatment components (eg, mindfulness, psychoeducation and action plans).59”
From Optimising psychological treatment for pain after breast cancer: a factorial design study protocol in Denmark (2023) · cites this paper as [59]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
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“The Multiphase Optimization Strategy (MOST) is a methodological framework, conceptually rooted in engineering, designed specifically for optimisation of multicomponent interventions.”
From Optimising psychological treatment for pain after breast cancer: a factorial design study protocol in Denmark (2023) · cites this paper as [59]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
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“Specifically, MOST enables the systematic identification of active treatment components in multicomponent interventions, including how the presence or absence of one or more components affects the performance of other components.59–61”
From Optimising psychological treatment for pain after breast cancer: a factorial design study protocol in Denmark (2023) · cites this paper as [59]
Experimental — accuracy not yet measuredIn the citing paper
“System identification approaches are used in engineering to obtain dynamic systems models; these in turn are the basis for the design of control systems which achieve optimization. 45”
From Evaluating Digital Health Interventions (2016) · cites this paper as [45]
In the citing paper
“MOST’s preparatory and optimization phases are more efficient for intervention development than a traditional randomized controlled trial (RCT) since it would take substantially greater resources (time and sample size per RCT) to meet this goal [37].”
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Citations
Cites
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A Factorial Trial of Six Interventions for the Prevention of Postoperative Nausea and Vomiting
Apfel, C. C., Korttila, K. T., Abdalla, M., Kerger, H., Turan, A., Vedder, I., Zernak, C., Danner, K., Jokela, R. M., Pocock, S., et al. (2004) · New England Journal of Medicine · doi:10.1056/nejmoa032196 · Cited by 1,550
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Ryan, T. P. (2007) · Wiley eBooks · doi:10.1002/9780470187548.ch12 · Cited by 1,435
Experiments - Planning, Analysis and Parameter Design Optimization
(2001) · Journal of Tropical Pediatrics · doi:10.1093/tropej/47.2.126 · Cited by 1,140
Collins, L. M., Murphy, S. A., & Strecher, V. (2007) · American Journal of Preventive Medicine · doi:10.1016/j.amepre.2007.01.022 · Cited by 1,082
Self-citationDesign of Experiments: Statistical Principles of Research Design and Analysis
Kuehl, R. O. (1999) · Cited by 908
Cited by
Sorted by citations
Michie, S., Yardley, L., West, R., Patrick, K., & Greaves, F. (2017) · Journal of Medical Internet Research · doi:10.2196/jmir.7126 · Cited by 865
“Theories, models, and methods to support this approach can be found in systems engineering and related sectors, for example, control theory [8], use of factorial or fractionated evaluation designs [9], and system optimization strategies [10] (please refer to [2] and Multimedia Appendix 2 for more details).”
MethodEvaluating Digital Health Interventions
Murray, E., Hekler, E. B., Andersson, G., Collins, L. M., Doherty, A., Hollis, C., Rivera, D. E., West, R., & Wyatt, J. C. (2016) · American Journal of Preventive Medicine · doi:10.1016/j.amepre.2016.06.008 · Cited by 847
Self-citationThe Lancet Commission on the future of care and clinical research in autism
Lord, C., Charman, T., Havdahl, A., Carbone, P., Anagnostou, E., Boyd, B., Carr, T., de Vries, P. J., Dissanayake, C., Divan, G., et al. (2022) · The Lancet · doi:10.1016/s0140-6736(21)01541-5 · Cited by 924
Internet-delivered psychological treatments: from innovation to implementation
Andersson, G., Titov, N., Dear, B. F., Rozental, A., & Carlbring, P. (2019) · World Psychiatry · doi:10.1002/wps.20610 · Cited by 750
The impact of digital technology on psychological treatments and their dissemination
Fairburn, C. G., & Patel, V. (2017) · Behaviour Research and Therapy · doi:10.1016/j.brat.2016.08.012 · Cited by 448
Metadata
- Authors
- Collins, L. M., Dziak, J. J., Kugler, K. C., & Trail, J. B.
- Affiliations
- Pennsylvania State University (US)
- Corresponding author
- Collins, Linda M.
- Venue
- American Journal of Preventive Medicine
- Publisher
- Elsevier BV
- Published
- 2014-10
- Volume
- 47
- Issue
- 4
- Pages
- 498-504
- ISSN
- 0749-3797 (ISSN-L)
- PMC
- PMC4171184
- OpenAlex
- W106021680
- Cited by
- 335
- References
- 27
- Citations by year
- 2026: 29 · 2025: 24 · 2024: 33 · 2023: 35 · 2022: 27 · 2021: 36 · 2020: 53 · 2019: 31 · 2018: 26 · 2017: 21 · 2016: 10 · 2015: 10
- FWCI
- 17.17
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- American Journal of Preventive Medicine (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institute on Drug Abuse (P50DA010075)
- National Cancer Institute (P50CA143188)
- National Center for Advancing Translational Sciences (KL2TR000126)
- National Institute of Diabetes and Digestive and Kidney Diseases (R01DK097364)
- 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
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Keywords
- factorial design, multicomponent intervention, intervention components, interaction effects, statistical power, resource management, experimental design, randomized controlled trials
- MeSH terms
- Humans, Preventive Health Services, Preventive Medicine, Research Design, Randomized Controlled Trials as Topic
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