Accounting for Hidden Bias in Vaccine Studies: A Negative Control Framework

项目摘要/摘要 提出的研究旨在开发新的因果推理方法来解决不可测量的混淆 通过利用所谓的阴性对照变量来影响疫苗有效性和安全性研究的已知偏差 在疫苗研究中广泛使用。负面控制结果是已知不受因果影响的变量 感兴趣的治疗，而阴性对照暴露是已知不会对结果产生因果影响的变量 感兴趣的人。作为主要利益的暴露-结果对，两者都有共同的混淆机制。 阴性对照的例子在疫苗研究中比比皆是。这种已知为零的效应构成了FalsifiCA的基础。 检测未测量的混杂的控制策略，然而，关于何时以及如何进行负面控制知之甚少 可用于解决未测量的混杂偏差。我们计划开发原则性的阴性对照方法 对于在存在不可测量的混杂的情况下的因果效应的同一性fi阳离子和半参数估计， 融合了现代高度适应性的机器学习方法。我们还计划开发阴性对照冰毒- 用于检测和量化对疫苗研究至关重要的复杂纵向和生存环境中的因果效应 使用常规收集的医疗保健数据。最后，我们计划将所提出的方法应用于疫苗评估。 使用从开创性的阴性试验设计平台收集的数据并监测疫苗安全性的有效性 使用电子健康记录数据。成功完成拟议的研究将使调查人员具备 转变范式的方法来释放当代医疗数据的全部潜力，鼓励调查人员 定期检查混淆偏见的证据，并最终提高科学fic研究的有效性。

Project Summary / Abstract The proposed research aims to develop novel causal inference methods to resolve unmeasured confounding bias known to plague vaccine effectiveness and safety studies by leveraging so-called negative control variables widely available in vaccine studies. A negative control outcome is a variable known not to be causally affected by the treatment of interest, while a negative control exposure is a variable known not to causally affect the outcome of interest. Both share a common confounding mechanism as the exposure-outcome pair of primary interest. Examples of negative controls abound in vaccine studies. Such known-null effects form the basis of falsifica- tion strategy to detect unmeasured confounding, however little is known about when and how negative controls can be used to resolve unmeasured confounding bias. We plan to develop principled negative control methods for identification and semiparametric estimation of causal effects in the presence of unmeasured confounding, incorporating modern highly adaptive machine learning methods. We also plan to develop negative control meth- ods to detect and quantify causal effects in complex longitudinal and survival settings critical to vaccine studies using routinely collected healthcare data. Finally we plan to apply the proposed methods to evaluate vaccine effectiveness using data collected from a pioneering test-negative design platform and to monitor vaccine safety using electronic health record data. Successful completion of the proposed research will equip investigators with paradigm-shifting methods to unlock the full potential of contemporary healthcare data, encourage investigators to routinely check for evidence of confounding bias, and ultimately improve the validity of scientific research.