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

疫苗研究中隐藏偏差的解释：阴性对照框架

• 批准号: 10322983
• 负责人: Xu Shi
• 金额: $ 36.6万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-01-01 至 2024-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10322983
• 关键词: Accounting; Affect; Area; Benefits and Risks; Complex; Data; Data Set; Dimensions; Electronic Health Record; Ensure; Evaluation; Event; Failure; Gene Expression Profiling; Health Policy; Healthcare; Knowledge; Machine Learning; Methods; Modernization; Monitor; Nature; Observational Study; Outcome; Plague; Plague Vaccine; Property; Public Health; Recording of previous events; Reproducibility; Research; Research Design; Research Personnel; Residual state; Risk; Statistical Methods; Techniques; Testing; Time; Tissue-Specific Gene Expression; Use Effectiveness; Vaccination; Vaccines; adverse event risk; design; effectiveness evaluation; effectiveness study; flexibility; high dimensionality; improved; innovation; interest; machine learning method; novel; pathogen; public health priorities; safety study; semiparametric; theories; tool; user friendly software; vaccine effectiveness; vaccine evaluation; vaccine safety; vaccine trial

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.

项目摘要 /摘要 拟议的研究旨在开发新的因果推理方法来解决未得到的混杂 通过利用所谓的负面对照变量，已知遭受疫苗有效性和安全性研究已知的偏见 在疫苗研究中广泛使用。负面控制结果是已知不小心影响的变量 感兴趣的治疗，而阴性控制暴露是已知的变量，不影响结果 感兴趣的。两者都具有共同的混杂机制，作为主要利益的暴露结果对。 疫苗研究中的阴性对照的例子很多。这种已知无效的效应构成了伪造的基础 检测未衡量混杂的策略，无论何时以及如何进行负面对照，知之甚少 可用于解决未衡量的混杂偏见。我们计划开发主要的负面对照方法 为了识别和半参数估计因果效应，在存在未衡量的混杂的情况下 结合现代高度自适应的机器学习方法。我们还计划开发负面控制方法 - 在复杂的纵向和生存环境中检测和量化因果研究至关重要的疫苗研究中的因果关系的ODS 使用常规收集的医疗数据。最后，我们计划应用提出的方法来评估疫苗 使用从开创性测试阴性设计平台收集的数据并监视疫苗安全的有效性 使用电子健康记录数据。拟议研究的成功完成将使调查人员配备 范式转移方法可以解锁当代医疗保健数据的全部潜力，鼓励调查人员 定期检查是否有混淆偏见的证据，并最终提高科学研究的有效性。