Targeted maximum likelihood estimation for causal inference in survival and competing risks analysis

DOI10.1007/s10985-022-09576-2OpenAlexW4308371965MaRDI QIDQ6205042

Helene C. W. Rytgaard, Mark J. Van der Laan

Publication date: 11 April 2024

Published in: Lifetime Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10985-022-09576-2

zbMATH Keywords

survival analysiscompeting riskssemiparametric efficiencycausal inferenceaverage treatment effectssuper learningTMLEhighly adaptive lasso

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Survival analysis and censored data (62Nxx)

Cites Work

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• A class of k-sample tests for comparing the cumulative incidence of a competing risk
• Targeted learning in data science. Causal inference for complex longitudinal studies
• Causal inference for complex longitudinal data: the continuous case.
• Unified methods for censored longitudinal data and causality
• Inefficient estimators of the bivariate survival function for three models
• Weak convergence and empirical processes. With applications to statistics
• Correcting an estimator of a multivariate monotone function with isotonic regression
• Likelihood Ratio-Based Confidence Intervals in Survival Analysis
• Oracle inequalities for multi-fold cross validation
• Partial likelihood
• Asymptotic Statistics
• A new approach to causal inference in mortality studies with a sustained exposure period—application to control of the healthy worker survivor effect
• One‐step targeted maximum likelihood estimation for time‐to‐event outcomes
• Super Learner
• Statistical models based on counting processes