Streamlined Estimation for Static, Dynamic and Stochastic Treatment Regimes in Longitudinal Data
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Updated
Mar 28, 2024 - R
Streamlined Estimation for Static, Dynamic and Stochastic Treatment Regimes in Longitudinal Data
The R package trajmsm is based on the paper Marginal Structural Models with Latent Class Growth Analysis of Treatment Trajectories: https://doi.org/10.48550/arXiv.2105.12720.
Targeted Learning for Survival Analysis
R functions for project setup, data cleaning, machine learning, SuperLearner, parallelization, and targeted learning.
Semiparametric inference for relative heterogeneous vaccine efficacy between strains in observational case-only studies
Variable importance through targeted causal inference, with Alan Hubbard
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Nonparametric estimators of the average treatment effect with doubly-robust confidence intervals and hypothesis tests
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Estimators of cross-validated prediction metrics with improved small sample performance
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Transporting intervention effects from one population to another with targeted learning
Collaborative Targeted Maximum Likelihood Estimation
SuperLearner R package: prediction model ensembling method
R code for evaluating adult HIV incidence, health, & implementation outcomes for the first phase of the SEARCH Study (https://www.searchendaids.com/). Full statistical analysis plan available at https://arxiv.org/abs/1808.03231
Reproduce the simulations in Cai W, van der Laan MJ (2019+). One-step TMLE for time-to-event outcomes.
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