Translating questions to estimands in randomized clinical trials with intercurrent events
将伴发事件下的随机临床试验问题转化为目标估计量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：摘要围绕随机试验的因果目标量、处理后事件和识别假设展开，因此直接对应因果推断，未提供其他细分的明确依据。
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
将伴发事件下的随机临床试验问题转化为目标估计量
- 研究问题
- 随机试验中出现处理后伴发事件时，如何把决策者或药物开发者的实际问题转化为具有清晰干预含义的因果目标量？
- 方法与思路
- 作者从当前决策与未来药物开发出发，在五个例子中讨论目标量选择，提出条件效应、序贯方案效应及可分离效应等替代。
- 材料陈述的贡献
- 摘要称，实用目标量应对应明确干预和可识别人群，所提替代目标量相较常用主层效应需要较弱的识别假设。
- 阅读关注点
- 应核对每个研究问题对应的干预与人群、伴发事件的处理方式，以及不同目标量识别假设的逐项比较和可实施性。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收均未核实；摘要未列出各目标量完整定义与假设，较弱假设的比较不能普遍化。
原文摘要
Intercurrent (post-treatment) events occur frequently in randomized trials, and investigators often express interest in treatment effects that suitably take account of these events. A naive conditioning on intercurrent events does not have a straight-forward causal interpretation, and the practical relevance of other commonly used approaches is debated. In this work, we discuss how to formulate and choose an estimand, beyond the marginal intention to treat effect, from the point of view of a decision maker and drug developer. In particular, we argue that careful articulation of a practically useful research question should either reflect decision making at this point in time or future drug development. Indeed, a substantially interesting estimand is a formalization of the (plain English) description of a research question. A common feature of estimands that are practically useful is that they correspond to possibly hypothetical but well-defined interventions in identifiable (sub)populations. To illustrate our points, we consider five examples that were recently used to motivate consideration of principal stratum estimands in clinical trials. In all of these examples, we propose alternative causal estimands, such as conditional effects, sequential regime effects and separable effects, that correspond to explicit research questions of substantial interest. Certain questions require stronger assumptions for identification. However, we highlight that our proposed estimands require less stringent assumptions than estimands commonly targeted in these settings, including principal stratum effects.