跳到正文
← 返回论文列表与筛选
阅读说明

本页整理公开书目信息,不生成学术质量评分。AI 解读根据题名及所注明的摘要或原文片段生成,未完成全文核验,可能有误;具体假设、证明与结论请核对原文。摘要保留来源文本,未经模型改写。

DOI 首次登记不等于接收或发表日期。部分更正、评论等条目没有独立摘要;缺少可靠材料时保留待补状态。

本页为预印本版本;arXiv 收录、作者填写的期刊信息以及作者既有发表经历,都不单独证明本篇已接收。作者发表记录按 OpenAlex 身份与完整姓名、本站覆盖期刊 ISSN 及正式卷期核对,只展示已核实例,档案仍可能误合并。

引用量采用 OpenAlex 单条关联记录,不合计多个记录;关联记录可能包含正式发表版本,不等于此版本单独引用。缺失不视为零,引用指标不代表论文质量。

关注作者按书目中的完整姓名匹配,同名和姓名变体需人工辨别。相关论文先按共享细分方向和具体方法排列,再参考通用方法与发表日期。

分类依据与纠错

AI 根据所提供的论文材料归类:摘要明确以微随机试验识别时变干预组件的因果效应,并讨论相应估计与检验,因此归入因果推断方向。

反馈分类问题

The Micro-Randomized Trial for Developing Digital Interventions: Experimental Design and Data Analysis Considerations

开发数字干预的微随机试验:实验设计与数据分析考量

正在核对关联版本…

代码、数据与补充材料

正在核对对应版本的资源…

关注这篇论文的作者
Tianchen Qian
Ashley E. Walton
Linda M. Collins
Predrag Klasnja
Stephanie T. Lanza
Inbal Nahum-Shani
Mashifiqui Rabbi
Michael A. Russell
Maureen A. Walton
Hyesun Yoo
Susan A. Murphy

开发数字干预的微随机试验:实验设计与数据分析考量

研究问题
如何利用微随机试验判断即时自适应干预的组件是否有效、在何种情境下有效,并据此指导数字干预的设计与分析?
方法与思路
作者讨论反复随机化试验的设计实施,定义因果游离效应,介绍加权中心化最小二乘估计与检验,并用三个试验说明。
材料陈述的贡献
摘要称,文章提供适用于主要及次要分析的因果效应定义,回顾具有一致性的 WCLS 估计方法,并说明标准统计软件中的实现。
阅读关注点
核对因果游离效应对应的干预比较、随机化和参与资格规则,以及 WCLS 一致性、检验有效性与重复观测处理所需条件。
阅读边界
这是 arXiv 预印本,同行评审或期刊接收未获核实;摘要未列出完整识别条件,也未提供各试验的效果数值或分析细节。

原文摘要

Just-in-time adaptive interventions (JITAIs) are time-varying adaptive interventions that use frequent opportunities for the intervention to be adapted--weekly, daily, or even many times a day. The micro-randomized trial (MRT) has emerged for use in informing the construction of JITAIs. MRTs can be used to address research questions about whether and under what circumstances JITAI components are effective, with the ultimate objective of developing effective and efficient JITAI. The purpose of this article is to clarify why, when, and how to use MRTs; to highlight elements that must be considered when designing and implementing an MRT; and to review primary and secondary analyses methods for MRTs. We briefly review key elements of JITAIs and discuss a variety of considerations that go into planning and designing an MRT. We provide a definition of causal excursion effects suitable for use in primary and secondary analyses of MRT data to inform JITAI development. We review the weighted and centered least-squares (WCLS) estimator which provides consistent causal excursion effect estimators from MRT data. We describe how the WCLS estimator along with associated test statistics can be obtained using standard statistical software such as R (R Core Team, 2019). Throughout we illustrate the MRT design and analyses using the HeartSteps MRT, for developing a JITAI to increase physical activity among sedentary individuals. We supplement the HeartSteps MRT with two other MRTs, SARA and BariFit, each of which highlights different research questions that can be addressed using the MRT and experimental design considerations that might arise.

笔记与分组