Factorial experiments: efficient tools for evaluation of intervention components
Linda M Collins, John J Dziak, Kari C Kugler, Jessica B Trail, Linda M Collins, John J Dziak, Kari C Kugler, Jessica B Trail
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.
Copyright © 2014 American Journal of Preventive Medicine. Published by Elsevier Inc. All rights reserved.
Source: PubMed