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

> **NIH NIH R01** · UNIVERSITY OF MICHIGAN AT ANN ARBOR · 2022 · $365,983

## Abstract

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.

## Key facts

- **NIH application ID:** 10322983
- **Project number:** 5R01GM139926-02
- **Recipient organization:** UNIVERSITY OF MICHIGAN AT ANN ARBOR
- **Principal Investigator:** Xu Shi
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $365,983
- **Award type:** 5
- **Project period:** 2021-01-01 → 2024-11-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10322983

## Citation

> US National Institutes of Health, RePORTER application 10322983, Accounting for Hidden Bias in Vaccine Studies: A Negative Control Framework (5R01GM139926-02). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10322983. Licensed CC0.

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