Cited by 335Optimal Experimental Design MethodsStatistical Methods in Clinical TrialsAdvanced Causal Inference Techniques

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Factorial Experiments

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Collins, Linda M., Dziak, John J., Kugler, Kari C., Trail, Jessica B.·2014·American Journal of Preventive Medicine

DOI 10.1016/j.amepre.2014.06.021PMC PMC4171184

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Abstract

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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Authors: Collins, L. M., Dziak, J. J., Kugler, K. C., & Trail, J. B.
Affiliations: Pennsylvania State University (US)
Corresponding author: Collins, Linda M.
ORCID: Collins, Linda M.
Dziak, John J.
Kugler, Kari C.
Venue: American Journal of Preventive Medicine
Publisher: Elsevier BV
Published: 2014-10
Volume: 47
Issue: 4
Pages: 498-504
ISSN: 0749-3797 (ISSN-L)
DOI: 10.1016/j.amepre.2014.06.021
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
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
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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