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IES Grant

Title: Causal Mediation Analysis Under Partial Compliance in Single-Site and Multisite Randomized Trials
Center: NCER Year: 2020
Principal Investigator: Hong, Guanglei Awardee: University of Chicago
Program: Statistical and Research Methodology in Education      [Program Details]
Award Period: 3 years (09/01/2020 – 08/31/2023) Award Amount: $899,688
Type: Methodological Innovation Award Number: R305D200031
Description:

Co-Principal Investigators: Qin, Xu; Yang, Fan

Purpose: The purpose of this project is to develop analytic strategies that incorporate instrumental variables for identifying and estimating the average treatment effect (ATE), the moderated ATE, the mediated ATE, and the moderated mediation effects in a single-site randomized trial where the moderator of theoretical interest is individual predisposition for compliance. The research team will also develop approaches to causal moderation and mediation in cluster randomized trials to take into account site-level compliance and its potential effect on treatment effect heterogeneity in the ATE and in mediator effects. Extending the new set of approaches for cluster randomized trials, this study will compare a method-of-moment estimation approach with Bayesian random-effects models.

Project Activities: The researchers will use simulations to test the new techniques and incorporate the techniques into existing statistical software. They will also demonstrate the new approaches on two real datasets: the National Job Corps Study and the Head Start Impact Study. The manuscripts resulting from the research will be primarily sent to statistical journals, but the team also plans to incorporate the new techniques into existing R software they developed, an existing Stata package they developed, and provide the algorithm as stand-alone software for use in Windows. Each of the software updates/additions will be accompanied by corresponding changes to the existing user's guides for the research team's software packages.

Publications and Products

Cruz-Cortés, E., Yang, F., Juaréz-Colunga, E., Warsavage, T., & Ghosh, D. (2021). Comment on 'Statistical Modelling: the Two Cultures' by Leo Breiman. Observational Studies, 7(1), 41–57.

Hong, G., Yang, F., & Qin, X. (2023). Posttreatment confounding in causal mediation studies: A cutting-edge problem and a novel solution via sensitivity analysis. Biometrics, 79(2), 1042–1056.

Qin, X. (2023). Sample size and power calculations for causal mediation analysis: A Tutorial and Shiny App. Behavior Research Methods, 1–32.

Qin, X. & Yang, F. (2022). Simulation-Based Sensitivity Analysis for Causal Mediation Studies. Psychological Methods, 27(6), 1000–1013.


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