# Causal mediation analysis in mental health with mediator missingness

> **NIH NIH R03** · JOHNS HOPKINS UNIVERSITY · 2022 · $81,875

## Abstract

PROJECT SUMMARY
Examining the mechanisms behind exposures and interventions is crucial in mental health
research; doing so can help identify potential intervention targets and tailor interventions to
optimize outcomes. Causal mediation analysis is an important tool for this job. An important but
under-appreciated challenge in mediation analysis is missing data. Missing data are ubiquitous
(even in high quality mental health studies) and are more of a challenge in mediation analyses
than in standard analyses, due to the involvement of more variables. Missingness tends to be
worse when the analysis involves multiple mediators and/or when data collection at intermediate
time points (when mediators are measured) receives less attention than baseline and outcome
data collection. Since most methodological work assumes complete data, it is not yet well
understood how to appropriately handle missing data when conducting causal mediation
analysis. As the first step in removing this barrier to effective use of causal mediation analysis in
mental health researcher, this project will tackle the problem of mediator missingness, a
common problem in practice and a particularly important one to tackle for the validity of
mediation analyses. This project will develop two classes of methods for this purpose: (1) with
the estimating equations approach, we will transform each of a collection of full-data methods to
observed-data methods that are doubly robust – consistent if either a missingness model or a
model for some relevant function of the mediator given observed data is correct; (2) to respond
to the popularity of multiple imputation, based on each full-data method, we will develop
targeted multiple imputation procedures tailored to the method and the effects being estimated,
to minimize bias due to misspecification of the imputation model. The project seeks methods
that can be explained intuitively, and will pay special attention to communicating and
disseminating the methods developed to mental health researchers. For illustration, the
methods will be applied to an analysis of effects of a treatment for anxiety disorder and an
analysis of disparities in suicidality affecting sexual minority youth.

## Key facts

- **NIH application ID:** 10352521
- **Project number:** 1R03MH128634-01
- **Recipient organization:** JOHNS HOPKINS UNIVERSITY
- **Principal Investigator:** Trang Quynh Nguyen
- **Activity code:** R03 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $81,875
- **Award type:** 1
- **Project period:** 2022-01-14 → 2023-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10352521

## Citation

> US National Institutes of Health, RePORTER application 10352521, Causal mediation analysis in mental health with mediator missingness (1R03MH128634-01). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10352521. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*