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Cluster randomized trials with treatment noncompliance.存在治疗不依从情况的整群随机试验。
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An adaptive approach to family intervention: linking engagement in family-centered intervention to reductions in adolescent problem behavior.一种家庭干预的适应性方法:将参与以家庭为中心的干预与减少青少年问题行为联系起来。
J Consult Clin Psychol. 2007 Aug;75(4):568-79. doi: 10.1037/0022-006X.75.4.568.
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Delaying second births among adolescent mothers: a randomized, controlled trial of a home-based mentoring program.推迟青春期母亲的二胎生育:一项基于家庭的指导计划的随机对照试验。
Pediatrics. 2006 Oct;118(4):e1087-99. doi: 10.1542/peds.2005-2318.
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Estimating treatment effects from randomized clinical trials with noncompliance and loss to follow-up: the role of instrumental variable methods.从不依从和失访的随机临床试验中估计治疗效果:工具变量法的作用
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Simple maximum likelihood estimates of efficacy in randomized trials and before-and-after studies, with implications for meta-analysis.随机试验和前后对照研究中疗效的简单最大似然估计及其对荟萃分析的影响。
Stat Methods Med Res. 2005 Aug;14(4):349-67. doi: 10.1191/0962280205sm404oa.
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Likelihood methods for treatment noncompliance and subsequent nonresponse in randomized trials.随机试验中治疗不依从及后续无反应的似然方法。
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An extended general location model for causal inferences from data subject to noncompliance and missing values.一种用于对存在不依从性和缺失值的数据进行因果推断的扩展一般位置模型。
Biometrics. 2004 Sep;60(3):598-607. doi: 10.1111/j.0006-341X.2004.00208.x.
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Estimating psychological treatment effects from a randomised controlled trial with both non-compliance and loss to follow-up.从一项存在不依从和失访情况的随机对照试验中估计心理治疗效果。
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Propensity score matching: an illustrative analysis of dose response.倾向评分匹配:剂量反应的实例分析
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Assessing the effect of an influenza vaccine in an encouragement design.在鼓励性设计中评估流感疫苗的效果。
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评估预防项目的干预效果:考虑不依从情况。

Estimating intervention effects of prevention programs: accounting for noncompliance.

作者信息

Stuart Elizabeth A, Perry Deborah F, Le Huynh-Nhu, Ialongo Nicholas S

机构信息

Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, 624 N Broadway, 8th Floor, Baltimore, MD 21205, USA.

出版信息

Prev Sci. 2008 Dec;9(4):288-98. doi: 10.1007/s11121-008-0104-y. Epub 2008 Oct 9.

DOI:10.1007/s11121-008-0104-y
PMID:18843535
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC2921838/
Abstract

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.

摘要

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。