Stuart Elizabeth A, Perry Deborah F, Le Huynh-Nhu, Ialongo Nicholas S
Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, 624 N Broadway, 8th Floor, Baltimore, MD 21205, USA.
Prev Sci. 2008 Dec;9(4):288-98. doi: 10.1007/s11121-008-0104-y. Epub 2008 Oct 9.
Individuals not fully complying with their assigned treatments is a common problem encountered in randomized evaluations of behavioral interventions. Treatment group members rarely attend all sessions or do all "required" activities; control group members sometimes find ways to participate in aspects of the intervention. As a result, there is often interest in estimating both the effect of being assigned to participate in the intervention, as well as the impact of actually participating and doing all of the required activities. Methods known broadly as "complier average causal effects" (CACE) or "instrumental variables" (IV) methods have been developed to estimate this latter effect, but they are more commonly applied in medical and treatment research. Since the use of these statistical techniques in prevention trials has been less widespread, many prevention scientists may not be familiar with the underlying assumptions and limitations of CACE and IV approaches. This paper provides an introduction to these methods, described in the context of randomized controlled trials of two preventive interventions: one for perinatal depression among at-risk women and the other for aggressive disruptive behavior in children. Through these case studies, the underlying assumptions and limitations of these methods are highlighted.
在行为干预的随机评估中,个体未完全遵守指定治疗方案是一个常见问题。治疗组成员很少参加所有疗程或完成所有“要求的”活动;对照组成员有时会设法参与干预的某些方面。因此,人们通常既感兴趣于估计被分配参与干预的效果,也感兴趣于实际参与并完成所有要求活动的影响。被广泛称为“依从者平均因果效应”(CACE)或“工具变量”(IV)方法的手段已被开发出来以估计后一种效应,但它们更常用于医学和治疗研究。由于这些统计技术在预防试验中的应用不太广泛,许多预防科学家可能不熟悉CACE和IV方法的潜在假设和局限性。本文在两项预防性干预措施的随机对照试验背景下介绍这些方法,一项针对高危女性的围产期抑郁症,另一项针对儿童的攻击性行为。通过这些案例研究,突出了这些方法的潜在假设和局限性。