Abstract
Two methods from the potential outcomes framework–inverse propensity weighting (IPW) and sequential G-estimation–were evaluated and compared to linear regression for estimating the mediated effect in a two-wave design with a randomized intervention and continuous mediator and outcome. Baseline measures of the mediator and outcome can be considered confounders of the follow-up mediator–outcome relation for which adjustment is necessary to eliminate bias. To adjust for baseline measures of the mediator and outcome, IPW uses stabilized inverse propensity weights whereas sequential G-estimation uses regression adjustment. Theoretical differences between the models are described, and Monte Carlo simulations compared the performance of linear regression; IPW without weight truncation; IPW with weights truncated at the 1st/99th, 5th/95th, and 10th/90th percentiles; and sequential G-estimation. Sequential G-estimation performed similarly to linear regression, but IPW provided a biased estimate of the mediated effect, lower power, lower confidence interval coverage, and higher mean squared error. Simulation results show that IPW failed to fully adjust the follow-up mediator–outcome relation for confounding due to the baseline measures. We then compared the mediated effect estimates using data from a randomized experiment evaluating a steroid prevention program for high school athletes. Implications and future directions are discussed.
Original language | English (US) |
---|---|
Pages (from-to) | 165-187 |
Number of pages | 23 |
Journal | Multivariate Behavioral Research |
Volume | 55 |
Issue number | 2 |
DOIs | |
State | Published - Mar 3 2020 |
Keywords
- Causal mediation
- inverse propensity weighting
- longitudinal mediation
- potential outcomes framework
- sequential G-estimation
ASJC Scopus subject areas
- Statistics and Probability
- Experimental and Cognitive Psychology
- Arts and Humanities (miscellaneous)
Fingerprint
Dive into the research topics of 'A Viable Alternative When Propensity Scores Fail: Evaluation of Inverse Propensity Weighting and Sequential G-Estimation in a Two-Wave Mediation Model'. Together they form a unique fingerprint.Datasets
-
A Viable Alternative When Propensity Scores Fail: Evaluation of Inverse Propensity Weighting and Sequential G-Estimation in a Two-Wave Mediation Model
Valente, M. J. (Contributor), Mazza, G. L. (Contributor) & MacKinnon, D. (Contributor), figshare Academic Research System, Jan 1 2020
DOI: 10.6084/m9.figshare.12162066.v1, https://doi.org/10.6084%2Fm9.figshare.12162066.v1
Dataset