Collins Linda M, Chakraborty Bibhas, Murphy Susan A, Strecher Victor
The Methodology Center and Department of Human Development and Family Studies, Penn State, University Park, PA 16801, USA.
Clin Trials. 2009 Feb;6(1):5-15. doi: 10.1177/1740774508100973.
Many interventions in today's health sciences are multicomponent, and often one or more of the components are behavioral. Two approaches to building behavioral interventions empirically can be identified. The more typically used approach, labeled here the classical approach, consists of constructing a likely best intervention a priori, and then evaluating the intervention in a standard randomized controlled trial (RCT). By contrast, the emergent phased experimental approach involves programmatic phases of empirical research and discovery aimed at identifying individual intervention component effects and the best combination of components and levels.
The purpose of this article is to provide a head-to-head comparison between the classical and phased experimental approaches and thereby highlight the relative advantages and disadvantages of these approaches when they are used to select program components and levels so as to arrive at the most potent intervention.
A computer simulation was performed in which the classical and phased experimental approaches to intervention development were applied to the same randomly generated data.
The phased experimental approach resulted in better mean intervention outcomes when the intervention effect size was medium or large, whereas the classical approach resulted in better mean intervention outcomes when the effect size was small. The phased experimental approach led to identification of the correct set of intervention components and levels at a higher rate than the classical approach across all conditions.
Some potentially important factors were not varied in the simulation, for example the underlying structural model and the number of intervention components.
The phased experimental approach merits serious consideration, because it has the potential to enable intervention scientists to develop more efficacious behavioral interventions.
当今健康科学领域的许多干预措施都是多成分的,而且其中一个或多个成分通常是行为方面的。可以确定两种基于实证构建行为干预措施的方法。这里标记为经典方法的更常用方法包括先验构建一个可能的最佳干预措施,然后在标准随机对照试验(RCT)中评估该干预措施。相比之下,新兴的分阶段实验方法涉及实证研究和发现的程序性阶段,旨在确定各个干预成分的效果以及成分和水平的最佳组合。
本文的目的是对经典方法和分阶段实验方法进行直接比较,从而突出这些方法在用于选择项目成分和水平以得出最有效的干预措施时的相对优缺点。
进行了一项计算机模拟,将经典的和分阶段实验的干预开发方法应用于相同的随机生成数据。
当干预效应量为中等或大时,分阶段实验方法产生的平均干预结果更好,而当效应量小时,经典方法产生的平均干预结果更好。在所有条件下,分阶段实验方法比经典方法更能以更高的概率识别出正确的干预成分和水平组合。
模拟中未对一些潜在的重要因素进行变化,例如潜在的结构模型和干预成分的数量。
分阶段实验方法值得认真考虑,因为它有可能使干预科学家开发出更有效的行为干预措施。