A semiparametric multiply robust multiple imputation method for causal inference.

Metrika

Department of Mathematics and Statistics, University of Ottawa, Ontario, Ottawa, ON, K1N 6N5, Canada.

Published: July 2023

Evaluating the impact of non-randomized treatment on various health outcomes is difficult in observational studies because of the presence of covariates that may affect both the treatment or exposure received and the outcome of interest. In the present study, we develop a semiparametric multiply robust multiple imputation method for estimating average treatment effects in such studies. Our method combines information from multiple propensity score models and outcome regression models, and is multiply robust in that it produces consistent estimators for the average causal effects if at least one of the models is correctly specified. Our proposed estimators show promising performances even with incorrect models. Compared with existing fully parametric approaches, our proposed method is more robust against model misspecifications. Compared with fully non-parametric approaches, our proposed method does not have the problem of curse of dimensionality and achieves dimension reduction by combining information from multiple models. In addition, it is less sensitive to the extreme propensity score estimates compared with inverse propensity score weighted estimators and augmented estimators. The asymptotic properties of our method are developed and the simulation study shows the advantages of our proposed method compared with some existing methods in terms of balancing efficiency, bias, and coverage probability. Rubin's variance estimation formula can be used for estimating the variance of our proposed estimators. Finally, we apply our method to 2009-2010 National Health Nutrition and Examination Survey (NHANES) to examine the effect of exposure to perfluoroalkyl acids (PFAs) on kidney function.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11087065PMC
http://dx.doi.org/10.1007/s00184-022-00883-0DOI Listing

Publication Analysis

Top Keywords

multiply robust
12
propensity score
12
proposed method
12
semiparametric multiply
8
robust multiple
8
multiple imputation
8
method
8
imputation method
8
proposed estimators
8
compared existing
8

Similar Publications

The Nordic countries are among the most digitally advanced societies in the world. Past research suggests that both social support offline and interaction online are linked to adolescent psychological adjustment. However, less is known regarding the complex implications of distinctive sources of social support offline and online interaction for a broader range of indices of adolescent psychosocial well-being, including its contemporary forms such as social media addiction.

View Article and Find Full Text PDF

Causal Estimands and Multiply Robust Estimation of Mediated-Moderation.

Multivariate Behav Res

January 2025

Department of Educational Psychology, The University of Texas at Austin, Austin, TX, USA.

When studying effect heterogeneity between different subgroups (i.e., moderation), researchers are frequently interested in the mediation mechanisms underlying the heterogeneity, that is, the mediated moderation.

View Article and Find Full Text PDF

Budding yeast cells multiply by asymmetric cell division. During this process, the cell organelles are transported by myosin motors along the actin cytoskeleton into the growing bud, while at the same time some organelles must be retained in the mother cell. The ordered partitioning of organelles depends on highly regulated binding of motor proteins to cargo membranes.

View Article and Find Full Text PDF

New techniques for largescale neural recordings from diverse animals are reshaping comparative systems neuroscience. This growth necessitates fresh conceptual paradigms for comparing neural circuits and activity patterns. Here, we take a systems neuroscience approach to early neural evolution, emphasizing the importance of considering nervous systems as multiply modulated, continuous dynamical systems.

View Article and Find Full Text PDF

Analyses of cluster randomized trials (CRTs) can be complicated by informative missing outcome data. Methods such as inverse probability weighted generalized estimating equations have been proposed to account for informative missingness by weighing the observed individual outcome data in each cluster. These existing methods have focused on settings where missingness occurs at the individual level and each cluster has partially or fully observed individual outcomes.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!