Heterogeneous Mediation Analysis for Cox Proportional Hazards Model With Multiple Mediators.

heterogeneous effect multiple mediators survival outcome variable selection

Journal

Statistics in medicine

ISSN: 1097-0258

Titre abrégé: Stat Med

Pays: England

ID NLM: 8215016

Informations de publication

Date de publication:
28 Oct 2024

Historique:

revised: 22 08 2024

received: 29 04 2024

accepted: 19 09 2024

medline: 28 10 2024

pubmed: 28 10 2024

entrez: 28 10 2024

Statut: aheadofprint

Résumé

This study proposes a heterogeneous mediation analysis for survival data that accommodates multiple mediators and sparsity of the predictors. We introduce a joint modeling approach that links the mediation regression and proportional hazards models through Bayesian additive regression trees with shared typologies. The shared tree component is motivated by the fact that confounders and effect modifiers on the causal pathways linked by different mediators often overlap. A sparsity-inducing prior is incorporated to capture the most relevant confounders and effect modifiers on different causal pathways. The individual-specific interventional direct and indirect effects are derived on the scale of the logarithm of hazards and survival function. A Bayesian approach with an efficient Markov chain Monte Carlo algorithm is developed to estimate the conditional interventional effects through the Monte Carlo implementation of the mediation formula. Simulation studies are conducted to verify the empirical performance of the proposed method. An application to the ACTG175 study further demonstrates the method's utility in causal discovery and heterogeneity quantification.

Identifiants

DOI: 10.1002/sim.10239 PMID: 39466693

pubmed: 39466693

doi: 10.1002/sim.10239

doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

Subventions

Organisme : Research Grants Council of the Hong Kong Special Administrative Region

ID : 14303622

Informations de copyright

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