Causal Vaccine Effects on Binary Postinfection Outcomes
疫苗对感染后二元结局的因果效应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。
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：摘要明确研究处理后选择偏差、主分层以及疫苗效应的因果识别与估计。
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…
疫苗对感染后二元结局的因果效应
- 研究问题
- 只比较已感染者的疾病、死亡等结局会产生处理后选择偏差时，如何定义并估计疫苗对感染后二元结局的因果效应？
- 方法与思路
- 作者用主分层定义无论接受疫苗或控制都会感染者中的效应，指出常规假设不足以识别，再以选择模型导出闭式最大似然估计。
- 材料陈述的贡献
- 摘要称，选择模型可识别目标效应，并揭示其与常用估计量的关系及条件性界限；所述百日咳应用报告疾病严重度显著降低。
- 阅读关注点
- 应核对主分层效应尺度、选择模型的额外限制、不可识别性论证，以及意向治疗估计构成上界所需的感染保护条件。
- 阅读边界
- 仅据 OpenAlex 索引摘要，稿件版本未注明；识别与界限依赖附加模型及条件，摘要未提供应用数值，不能将感染者比较直接解释为因果效应。
原文摘要
The effects of vaccine on postinfection outcomes, such as disease, death, and secondary transmission to others, are important scientific and public health aspects of prophylactic vaccination. As a result, evaluation of many vaccine effects condition on being infected. Conditioning on an event that occurs posttreatment (in our case, infection subsequent to assignment to vaccine or control) can result in selection bias. Moreover, because the set of individuals who would become infected if vaccinated is likely not identical to the set of those who would become infected if given control, comparisons that condition on infection do not have a causal interpretation. In this article we consider identifiability and estimation of causal vaccine effects on binary postinfection outcomes. Using the principal stratification framework, we define a postinfection causal vaccine efficacy estimand in individuals who would be infected regardless of treatment assignment. The estimand is shown to be not identifiable under the standard assumptions of the stable unit treatment value, monotonicity, and independence of treatment assignment. Thus selection models are proposed that identify the causal estimand. Closed-form maximum likelihood estimators (MLEs) are then derived under these models, including those assuming maximum possible levels of positive and negative selection bias. These results show the relations between the MLE of the causal estimand and two commonly used estimators for vaccine effects on postinfection outcomes. For example, the usual intent-to-treat estimator is shown to be an upper bound on the postinfection causal vaccine effect provided that the magnitude of protection against infection is not too large. The methods are used to evaluate postinfection vaccine effects in a clinical trial of a rotavirus vaccine candidate and in a field study of a pertussis vaccine. Our results show that pertussis vaccination has a significant causal effect in reducing disease severity.