Friday, July 26, 2024
7/26/2024
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.
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