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  "Title": "Doubly Robust Inference for Cox Marginal Structural Model with\nInformative Censoring",
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  "Authors@R": "c(\nperson(\"Jiyu\", \"Luo\", email = \"charlesluo1002@gmail.com\", role = c(\"cre\",\"aut\")),\nperson(\"Dennis\", \"Rava\", email = \"drava@ucsd.edu\", role = \"aut\"),\nperson(\"Ronghui\", \"Xu\", email = \"rxu@health.ucsd.edu\", role = \"aut\"))",
  "Description": "Doubly robust estimation and inference of log hazard ratio\nunder the Cox marginal structural model with informative\ncensoring. An augmented inverse probability weighted estimator\nthat involves 3 working models, one for conditional failure\ntime T, one for conditional censoring time C and one for\npropensity score. Both models for T and C can depend on both a\nbinary treatment A and additional baseline covariates Z, while\nthe propensity score model only depends on Z. With the help of\ncross-fitting techniques, achieves the rate-doubly robust\nproperty that allows the use of most machine learning or\nnon-parametric methods for all 3 working models, which are not\npermitted in classic inverse probability weighting or doubly\nrobust estimators. When the proportional hazard assumption is\nviolated, CoxAIPW estimates a causal estimated that is a\nweighted average of the time-varying log hazard ratio.\nReference: Luo, J. (2023). Statistical Robustness - Distributed\nLinear Regression, Informative Censoring, Causal Inference, and\nNon-Proportional Hazards [Unpublished doctoral dissertation].\nUniversity of California San Diego.; Luo & Xu (2022)\n<doi:10.48550/arXiv.2206.02296>; Rava (2021)\n<https://escholarship.org/uc/item/8h1846gs>.",
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