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Extending the principal stratification method to multi-level randomized trials.

机译:将主要分层方法扩展到多级随机试验。

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摘要

The Principal Stratification method estimates a causal intervention effect by taking account of subjects' differences in participation, adherence or compliance. The current Principal Stratification method has been mostly used in randomized intervention trials with randomization at a single (individual) level with subjects who were randomly assigned to either intervention or control condition. However, randomized intervention trials have been conducted at group level instead of individual level in many scientific fields. This is so called "two-level randomization", where randomization is conducted at a group (second) level, above an individual level but outcome is often observed at individual level within each group. The incorrect inferences may result from the causal modeling if one only considers the compliance from individual level, but ignores it or be determine it from group level for a two-level randomized trial. The Principal Stratification method thus needs to be further developed to address this issue.;To extend application of the Principal Stratification method, this research developed a new methodology for causal inferences in two-level intervention trials which principal stratification can be formed by both group level and individual level compliance. Built on the original Principal Stratification method, the new method incorporates a range of alternative methods to assess causal effects on a population when data on exposure at the group level are incomplete or limited, and are data at individual level. We use the Gatekeeper Training Trial, as a motivating example as well as for illustration. This study is focused on how to examine the intervention causal effect for schools that varied by level of adoption of the intervention program (Early-adopter vs. Later-adopter). In our case, the traditional Exclusion Restriction Assumption for Principal Stratification method is no longer hold. The results show that the intervention had a stronger impact on Later-Adopter group than Early-Adopter group for all participated schools. These impacts were larger for later trained schools than earlier trained schools. The study also shows that the intervention has a different impact on middle and high schools.
机译:首要分层法通过考虑受试者参与,依从性或依从性方面的差异来估计因果干预效果。当前的“主要分层”方法主要用于随机干预试验中,在单个(个体)水平上对受试者随机分配到干预或对照条件下。但是,在许多科学领域,随机干预试验都是在小组级别而不是个人级别进行的。这就是所谓的“两级随机化”,其中随机化是在高于单个水平的组(第二个)水平上进行的,但通常会在每个组中的单个水平上观察到结果。如果因果模型仅考虑个人水平的依从性,而忽略该因果模型,或者在小组水平的两级随机试验中确定它,则错误的推论可能是因果模型造成的。因此,需要进一步开发主体分层方法以解决该问题。为了扩展主体分层方法的应用,本研究开发了一种用于两级干预试验中因果推理的新方法,该方法可以由两个小组级别形成主体分层以及个人层面的合规性。在原始的“主要分层”方法的基础上,新方法结合了一系列替代方法,以评估在群体层次上的暴露数据不完整或有限且在个体水平上的数据时对人群的因果效应。我们使用Gatekeeper培训试用版作为激励示例和说明。这项研究的重点是如何检查因干预计划的采用水平而不同的学校的干预因果效应(早期采用者与后采用者)。在我们的案例中,不再适用传统的“主体分层排除限制假设”方法。结果表明,对于所有参与的学校,干预对后采纳者组的影响要比早期采纳者组的影响更大。这些影响对于后来受过训练的学校来说比早期受过训练的学校更大。研究还表明,干预对初中和高中有不同的影响。

著录项

  • 作者

    Guo, Jing.;

  • 作者单位

    University of South Florida.;

  • 授予单位 University of South Florida.;
  • 学科 Biology Biostatistics.
  • 学位 Ph.D.
  • 年度 2010
  • 页码 154 p.
  • 总页数 154
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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