220 citations · 393 across the 14 of their papers we have counts for
17 papers
Precision Mental Health: Predicting Heterogeneous Treatment Effects for Depression through Data Integration
Carly L. Brantner, Trang Quynh Nguyen, Harsh Parikh +3
When treating depression, clinicians are interested in determining the optimal treatment for a given patient, which is challenging given the amount of treatments available. To adva…
A Cautionary Tale on Integrating Studies with Disparate Outcome Measures for Causal Inference
Harsh Parikh, Trang Quynh Nguyen, Elizabeth A. Stuart +2
Data integration approaches are increasingly used to enhance the efficiency and generalizability of studies. However, a key limitation of these methods is the assumption that outco…
Self-separated and self-connected models for mediator and outcome missingness in mediation analysis
Trang Quynh Nguyen, Razieh Nabi, Fan Yang +2
Missing data is a common challenge in studying treatment effects. In the context of mediation analysis, this paper addresses missingness in the mediator and outcome, focusing on id…
Practical challenges in mediation analysis: A guide for applied researchers
Megan S. Schuler, Donna L. Coffman, Elizabeth A. Stuart +3
Mediation analysis is a statistical approach that can provide insights regarding the intermediary processes by which an intervention or exposure affects a given outcome. Mediation…
Identification of complier and noncomplier average causal effects in the presence of latent missing-at-random (LMAR) outcomes: a unifying view and choices of assumptions
Trang Quynh Nguyen, Michelle C. Carlson, Elizabeth A. Stuart
The study of treatment effects is often complicated by noncompliance and missing data. In the one-sided noncompliance setting where of interest are the complier and noncomplier ave…
Methods for Integrating Trials and Non-Experimental Data to Examine Treatment Effect Heterogeneity
Carly Lupton Brantner, Ting-Hsuan Chang, Trang Quynh Nguyen +3
Estimating treatment effects conditional on observed covariates can improve the ability to tailor treatments to particular individuals. Doing so effectively requires dealing with p…
Comparison of Methods that Combine Multiple Randomized Trials to Estimate Heterogeneous Treatment Effects
Carly Lupton Brantner, Trang Quynh Nguyen, Tengjie Tang +3
Individualized treatment decisions can improve health outcomes, but using data to make these decisions in a reliable, precise, and generalizable way is challenging with a single da…
Sensitivity analysis for principal ignorability violation in estimating complier and noncomplier average causal effects
Trang Quynh Nguyen, Elizabeth A. Stuart, Daniel O. Scharfstein +1
An important strategy for identifying principal causal effects, which are often used in settings with noncompliance, is to invoke the principal ignorability (PI) assumption. As PI…
Multiple imputation for propensity score analysis with covariates missing at random: some clarity on within and across methods
Trang Quynh Nguyen, Elizabeth A. Stuart
In epidemiology and social sciences, propensity score methods are popular for estimating treatment effects using observational data, and multiple imputation is popular for handling…
Causal mediation analysis: From simple to more robust strategies for estimation of marginal natural (in)direct effects
Trang Quynh Nguyen, Elizabeth L. Ogburn, Ian Schmid +4
This paper aims to provide practitioners of causal mediation analysis with a better understanding of estimation options. We take as inputs two familiar strategies (weighting and mo…
Sensitivity analyses for effect modifiers not observed in the target population when generalizing treatment effects from a randomized controlled trial: Assumptions, models, effect scales, data scenarios, and implementation details
Trang Quynh Nguyen, Benjamin Ackerman, Ian Schmid +2
Background: Randomized controlled trials are often used to inform policy and practice for broad populations. The average treatment effect (ATE) for a target population, however, ma…
Clarifying causal mediation analysis: Effect identification via three assumptions and five potential outcomes
Trang Quynh Nguyen, Ian Schmid, Elizabeth L. Ogburn +1
Causal mediation analysis is complicated with multiple effect definitions that require different sets of assumptions for identification. This paper provides a systematic explanatio…
Partially Pooled Propensity Score Models for Average Treatment Effect Estimation with Multilevel Data
Youjin Lee, Trang Q. Nguyen, Elizabeth A. Stuart
Causal inference analyses often use existing observational data, which in many cases has some clustering of individuals. In this paper we discuss propensity score weighting methods…
Propensity score analysis with latent covariates: Measurement error bias correction using the covariate's posterior mean, aka the inclusive factor score
Trang Quynh Nguyen, Elizabeth A. Stuart
We address measurement error bias in propensity score (PS) analysis due to covariates that are latent variables. In the setting where latent covariate is measured via multiple…
Clarifying causal mediation analysis for the applied researcher: Defining effects based on what we want to learn
Trang Quynh Nguyen, Ian Schmid, Elizabeth A. Stuart
The incorporation of causal inference in mediation analysis has led to theoretical and methodological advancements -- effect definitions with causal interpretation, clarification o…
Sensitivity analysis for an unobserved moderator in RCT-to-target-population generalization of treatment effects
Trang Quynh Nguyen, Cyrus Ebnesajjad, Stephen R. Cole +1
In the presence of treatment effect heterogeneity, the average treatment effect (ATE) in a randomized controlled trial (RCT) may differ from the average effect of the same treatmen…
The magnitude and direction of collider bias for binary variables
Trang Quynh Nguyen, Allan Dafoe, Elizabeth L. Ogburn
Suppose we are interested in the effect of variable on variable . If and both influence, or are associated with variables that influence, a common outcome, called a…