Lars van der Laan
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Lars van der Laan has a total of 5 co-authors. Here are the top co-authors by number of shared publications:
Kajsa Kvist
Division of Biostatistics School of Public Health University of California Berkeley Berkeley USA
1 shared publication- Mark J. van der Laan
University of California
1 shared publication - Maya L. Petersen
University of California Berkeley
1 shared publication - Thomas Aalexander Gerds
Department of Biostatistics, Faculty of Health Sciences, University of Copenhagen, Copenhagen, Denmark
1 shared publication - Zeyi Wang
Division of Biostatistics School of Public Health University of California Berkeley Berkeley USA
1 shared publication
Latest Publications
- Open Access
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, competing risks, and informative censoring, there exists a general desire to combine machine learning techniques and semiparametric theory. In this manuscript, 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 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.
Open AccessTargeted 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, competing risks, and informative censoring, there exists a general desire to combine machine learning techniques and semiparametric theory. In this manuscript, 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 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.