Nonlinear mediation analysis with high‐dimensional mediators whose causal structure is unknown

Autor: Wen Wei Loh, Beatrijs Moerkerke, Tom Loeys, Stijn Vansteelandt
Rok vydání: 2020
Předmět:
SELECTION
FOS: Computer and information sciences
Statistics and Probability
Counterfactual thinking
multiple mediation analysis
Mediation (statistics)
direct and indirect effects
Computer science
Genetics and Molecular Biology
marginal and conditional effects
Causal structure
effect decomposition
01 natural sciences
General Biochemistry
Genetics and Molecular Biology
Methodology (stat.ME)
010104 statistics & probability
03 medical and health sciences
Econometrics
path analysis
Humans
0101 mathematics
Path analysis (statistics)
Statistics - Methodology
030304 developmental biology
collapsibility
0303 health sciences
Mediation Analysis
Models
Statistical
General Immunology and Microbiology
Applied Mathematics
Nonparametric statistics
Probability and statistics
General Medicine
Causality
Nonlinear system
Mathematics and Statistics
Nonlinear Dynamics
General Biochemistry
REGULARIZATION
Monte Carlo integration
General Agricultural and Biological Sciences
Monte Carlo Method
Zdroj: BIOMETRICS
ISSN: 1541-0420
0006-341X
DOI: 10.1111/biom.13402
Popis: With multiple potential mediators on the causal pathway from a treatment to an outcome, we consider the problem of decomposing the effects along multiple possible causal path(s) through each distinct mediator. Under Pearl's path-specific effects framework (Pearl, 2001; Avin et al., 2005), such fine-grained decompositions necessitate stringent assumptions, such as correctly specifying the causal structure among the mediators, and there being no unobserved confounding among the mediators. In contrast, interventional direct and indirect effects for multiple mediators (Vansteelandt and Daniel, 2017) can be identified under much weaker conditions, while providing scientifically relevant causal interpretations. Nonetheless, current estimation approaches require (correctly) specifying a model for the joint mediator distribution, which can be difficult when there is a high-dimensional set of possibly continuous and non-continuous mediators. In this article, we avoid the need to model this distribution, by developing a definition of interventional effects previously suggested by VanderWeele and Tchetgen Tchetgen (2017) for longitudinal mediation. We propose a novel estimation strategy that uses non-parametric estimates of the (counterfactual) mediator distributions. Non-continuous outcomes can be accommodated using non-linear outcome models. Estimation proceeds via Monte Carlo integration. The procedure is illustrated using publicly available genomic data (Huang and Pan, 2016) to assess the causal effect of a microRNA expression on the three-month mortality of brain cancer patients that is potentially mediated by expression values of multiple genes.
30 pages, 2 figures, 3 tables
Databáze: OpenAIRE
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