Cluster randomized trials designed to support generalizable inferences
支持可推广推断的整群随机试验设计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：摘要明确研究整群随机试验向目标总体推广的潜在结果及平均处理效应。
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…
支持可推广推断的整群随机试验设计
- 研究问题
- 当试验因实施便利或亚组分析而对部分群体过度抽样时，如何将整群随机试验的处理效应推广到目标群体总体？
- 方法与思路
- 作者将随机试验嵌入符合资格的群体队列，按已知且可依赖群体特征的概率抽样，并开发总体效应分析方法。
- 材料陈述的贡献
- 摘要给出总体及未随机化群体子集的平均潜在结果与平均处理效应识别、估计结果，模拟报告低偏差但精度不同。
- 阅读关注点
- 应核对目标群体和效应加权单位、抽样概率的使用方式、推广所需条件，以及不同估计量精度差异的来源。
- 阅读边界
- 这是预印本，同行评审及期刊接收未核实；摘要未列出完整识别条件和方差公式，已知抽样概率本身不足以判断推广有效性。
原文摘要
Background: When planning a cluster randomized trial, evaluators often have access to an enumerated cohort representing the target population of clusters. Practicalities of conducting the trial, such as the need to oversample clusters with certain characteristics to improve trial economy or to support inference about subgroups of clusters, may preclude simple random sampling from the cohort into the trial, and thus interfere with the goal of producing generalizable inferences about the target population. Methods: We describe a nested trial design where the randomized clusters are embedded within a cohort of trial-eligible clusters from the target population and where clusters are selected for inclusion in the trial with known sampling probabilities that may depend on cluster characteristics (e.g., allowing clusters to be chosen to facilitate trial conduct or to examine hypotheses related to their characteristics). We develop and evaluate methods for analyzing data from this design to generalize causal inferences to the target population underlying the cohort. Results: We present identification and estimation results for the expectation of the average potential outcome and for the average treatment effect, in the entire target population of clusters and in its non-randomized subset. In simulation studies we show that all the estimators have low bias but markedly different precision. Conclusions: Cluster randomized trials where clusters are selected for inclusion with known sampling probabilities that depend on cluster characteristics, combined with efficient estimation methods, can precisely quantify treatment effects in the target population, while addressing objectives of trial conduct that require oversampling clusters on the basis of their characteristics.