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.
- Standort
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Deutsche Nationalbibliothek Frankfurt am Main
- Umfang
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Online-Ressource
- Sprache
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Englisch
- Erschienen in
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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)
- Urheber
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Wang, Zeyi
Laan, Lars van der
Petersen, Maya
Gerds, Thomas
Kvist, Kajsa
Laan, Mark van der
- DOI
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10.1515/jci-2023-0013
- URN
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urn:nbn:de:101:1-2502040439473.047320372279
- Rechteinformation
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Letzte Aktualisierung
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15.08.2025, 07:22 MESZ
Datenpartner
Deutsche Nationalbibliothek. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Beteiligte
- Wang, Zeyi
- Laan, Lars van der
- Petersen, Maya
- Gerds, Thomas
- Kvist, Kajsa
- Laan, Mark van der
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