Targeted maximum likelihood based estimation for longitudinal mediation analysis
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, com...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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De Gruyter
2025-01-01
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| Series: | Journal of Causal Inference |
| Subjects: | |
| Online Access: | https://doi.org/10.1515/jci-2023-0013 |
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| _version_ | 1832570433820426240 |
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| author | Wang Zeyi Laan Lars van der Petersen Maya Gerds Thomas Kvist Kajsa Laan Mark van der |
| author_facet | Wang Zeyi Laan Lars van der Petersen Maya Gerds Thomas Kvist Kajsa Laan Mark van der |
| author_sort | Wang Zeyi |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-6e622d5970f240fbb2519c0dae707ab5 |
| institution | Kabale University |
| issn | 2193-3685 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | De Gruyter |
| record_format | Article |
| series | Journal of Causal Inference |
| spelling | doaj-art-6e622d5970f240fbb2519c0dae707ab52025-02-02T15:45:47ZengDe GruyterJournal of Causal Inference2193-36852025-01-0113148284010.1515/jci-2023-0013Targeted maximum likelihood based estimation for longitudinal mediation analysisWang Zeyi0Laan Lars van der1Petersen Maya2Gerds Thomas3Kvist Kajsa4Laan Mark van der5Division of Biostatistics, School of Public Health, University of California, Berkeley, Berkeley, United States of AmericaDivision of Environmental Health Sciences, School of Public Health, University of California, Berkeley, Berkeley, United States of AmericaDivision of Biostatistics, School of Public Health, University of California, Berkeley, Berkeley, United States of AmericaDepartment of Public Health, Section of Biostatistics, University of Copenhagen, Copenhagen, DenmarkNovo Nordisk, Søborg, DenmarkDivision of Biostatistics, School of Public Health, University of California, Berkeley, Berkeley, United States of AmericaCausal 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.https://doi.org/10.1515/jci-2023-0013longitudinal mediation analysisstochastic interventionrandom interventiontargeted maximum likelihood estimationefficient influence curveefficient estimatorhighly adaptive lasso62g0562g20 |
| spellingShingle | Wang Zeyi Laan Lars van der Petersen Maya Gerds Thomas Kvist Kajsa Laan Mark van der Targeted maximum likelihood based estimation for longitudinal mediation analysis Journal of Causal Inference longitudinal mediation analysis stochastic intervention random intervention targeted maximum likelihood estimation efficient influence curve efficient estimator highly adaptive lasso 62g05 62g20 |
| title | Targeted maximum likelihood based estimation for longitudinal mediation analysis |
| title_full | Targeted maximum likelihood based estimation for longitudinal mediation analysis |
| title_fullStr | Targeted maximum likelihood based estimation for longitudinal mediation analysis |
| title_full_unstemmed | Targeted maximum likelihood based estimation for longitudinal mediation analysis |
| title_short | Targeted maximum likelihood based estimation for longitudinal mediation analysis |
| title_sort | targeted maximum likelihood based estimation for longitudinal mediation analysis |
| topic | longitudinal mediation analysis stochastic intervention random intervention targeted maximum likelihood estimation efficient influence curve efficient estimator highly adaptive lasso 62g05 62g20 |
| url | https://doi.org/10.1515/jci-2023-0013 |
| work_keys_str_mv | AT wangzeyi targetedmaximumlikelihoodbasedestimationforlongitudinalmediationanalysis AT laanlarsvander targetedmaximumlikelihoodbasedestimationforlongitudinalmediationanalysis AT petersenmaya targetedmaximumlikelihoodbasedestimationforlongitudinalmediationanalysis AT gerdsthomas targetedmaximumlikelihoodbasedestimationforlongitudinalmediationanalysis AT kvistkajsa targetedmaximumlikelihoodbasedestimationforlongitudinalmediationanalysis AT laanmarkvander targetedmaximumlikelihoodbasedestimationforlongitudinalmediationanalysis |