A Two-Stage Method for Extending Inferences from a Collection of Trials
将多项试验推断推广至目标总体的两阶段方法AI 解读 ↓
← 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：摘要明确研究随机试验效应向目标总体的因果推广与异质性加权，支持因果推断分类，未涉及面板或分位数模型。
反馈分类问题
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
将多项试验推断推广至目标总体的两阶段方法
- 研究问题
- 当多项随机试验的条件平均处理效应存在研究间异质性时，如何综合参与者数据，估计特定目标总体的平均处理效应？
- 方法与思路
- 作者提出目标总体效应可识别的一组假设，先得到各试验针对目标总体的效应估计，再通过第二阶段加权汇总。
- 材料陈述的贡献
- 摘要称，两阶段方法允许条件平均效应在研究间异质，并通过模拟及儿童创伤性脑损伤在线治疗试验集合进行评估。
- 阅读关注点
- 应核对试验到目标总体的推广假设、两阶段权重含义、研究间异质性的允许形式，以及估计不确定性的合并方法。
- 阅读边界
- 所给来源为 arXiv 预印本，同行评审未经核实；摘要未列识别假设、重叠要求或模拟结果，不能判断具体目标总体的适用性。
原文摘要
When considering the effect a treatment will cause in a population of interest, we often look to evidence from randomized controlled trials. In settings where multiple trials on a treatment are available, we may wish to synthesize the trials' participant data to obtain causally interpretable estimates of the average treatment effect in a specific target population. Traditional meta-analytic approaches to synthesizing data from multiple studies estimate the average effect among the studies. The resulting estimate is often not causally interpretable in any population, much less a particular population of interest, due to heterogeneity in the effect of treatment across studies. Inspired by traditional two-stage meta-analytic methods, as well as methods for extending inferences from a single study, we propose a two-stage approach to extending inferences from a collection of randomized controlled trials that can be used to obtain causally interpretable estimates of treatment effects in a target population when there is between-study heterogeneity in conditional average treatment effects. We first introduce a collection of assumptions under which the target population's average treatment effect is identifiable when conditional average treatment effects are heterogeneous across studies. We then introduce an estimator that utilizes weighting in two stages, taking a weighted average of study-specific estimates of the treatment effect in the target population. The performance of our proposed approach is assessed through simulation studies and an application to a collection of trials studying an online therapy treatment for symptoms of pediatric traumatic brain injury.