Targeted maximum likelihood based estimation for longitudinal mediation analysis

Abstract: Causal mediation analysis with random interventions has become an area of significant interest for understanding time-varying effects with longitudinal and survival outcomes. To tackle causal and statistical challenges due to the complex longitudinal data structure with time-varying confounders, competing risks, and informative censoring, there exists a general desire to combine machine learning techniques and semiparametric theory. In this article, we focus on targeted maximum likelihood estimation (TMLE) of longitudinal natural direct and indirect effects defined with random interventions. The proposed estimators are multiply robust, locally efficient, and directly estimate and update the conditional densities that factorize data likelihoods. We utilize the highly adaptive lasso (HAL) and projection representations to derive new estimators (HAL-EIC) of the efficient influence curves (EICs) of longitudinal mediation problems and propose a fast one-step TMLE algorithm using HAL-EIC while preserving the asymptotic properties. The proposed method can be generalized for other longitudinal causal parameters that are smooth functions of data likelihoods, and thereby provides a novel and flexible statistical toolbox.

Location
Deutsche Nationalbibliothek Frankfurt am Main
Extent
Online-Ressource
Language
Englisch

Bibliographic citation
Targeted maximum likelihood based estimation for longitudinal mediation analysis ; volume:13 ; number:1 ; year:2025 ; extent:39
Journal of causal inference ; 13, Heft 1 (2025) (gesamt 39)

Creator
Wang, Zeyi
Laan, Lars van der
Petersen, Maya
Gerds, Thomas
Kvist, Kajsa
Laan, Mark van der

DOI
10.1515/jci-2023-0013
URN
urn:nbn:de:101:1-2502040439473.047320372279
Rights
Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
Last update
15.08.2025, 7:22 AM CEST

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Associated

  • Wang, Zeyi
  • Laan, Lars van der
  • Petersen, Maya
  • Gerds, Thomas
  • Kvist, Kajsa
  • Laan, Mark van der

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