Factorial experiments: efficient tools for evaluation of intervention components.
Factorial experiments: efficient tools for evaluation of intervention components.
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DOI：
10.1016/j.amepre.2014.06.021
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发表时间：
2014-10
影响因子：
5.5
通讯作者：
Trail, Jessica B.
中科院分区：
文献类型：
--
作者：
Collins, Linda M.;Dziak, John J.;Kugler, Kari C.;Trail, Jessica B.
了解一组干预成分的个体和综合效应对于推动预防医学干预科学向前发展非常重要。这种理解通常可以通过析因实验以有效和经济的方式实现，其中操纵两个或多个自变量。析因实验是随机对照试验（RCT）的补充;两种设计解决不同的研究问题。本文提供了一个介绍析因实验，旨在调查人员培训主要是在随机对照试验。析因实验与干预科学中常用的其他实验设计进行了比较和对比，以突出每个实验设计最有效和最合适的地方。提出了以下几点：析因实验可以非常有效地利用实验对象时，数据进行了适当的分析;析因实验可以有很好的统计力量，即使它有相对较少的受试者，每一个实验条件;和进行研究时，选择组件包括在一个多组分干预，相互作用应加以研究，而不是避免。预防医学和相关领域的研究者应该开始考虑因子实验以及其他方法。应该从资源管理的角度来选择实验设计，即最好的实验设计是在不超过可用资源的情况下提供最大科学效益的设计。
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 randomized controlled trial (RCT); the two designs address different research questions. This article offers an introduction to factorial experiments aimed at investigators trained primarily in the RCT. 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. 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. 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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影响因子：
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作者：
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通讯作者：
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