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  "Title": "Cox Regression with Missing not at Random Failure Indicators",
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  "Authors@R": "c(\nperson(\"Shikhar\", \"Tyagi\", email = \"shikhar1093tyagi@gmail.com\",\nrole = c(\"aut\", \"cre\"), comment = c(ORCID = \"0000-0003-1606-0844\")),\nperson(\"Arvind\", \"Pandey\", email = \"arvindmzu@gmail.com\", role = \"aut\"),\nperson(\"Bhupendra\", \"Singh\", email = \"bhupendra.rana@gmail.com\", role = \"aut\"),\nperson(\"Vrijesh\", \"Tripathi\", email = \"vrijesh.tripathi@uwi.edu\", role = \"aut\")\n)",
  "Description": "Implements estimation for the Cox (1972, 1975)\n<doi:10.1111/j.2517-6161.1972.tb00899.x>\n<doi:10.1093/biomet/62.2.269> proportional hazards model when\nthe failure indicator (cause of failure) is missing not at\nrandom (MNAR), following the two adjusted imputation-based\nestimating equations of Liu and Liu (2026)\n<doi:10.1007/s11222-026-10857-1>. Also provided for comparison\nare the full-data partial-likelihood estimator of Andersen and\nGill (1982) <doi:10.1214/aos/1176345976>, the complete-case\nestimator, and the missing-at-random imputation estimator of\nLiu and Wang (2010, Statistica Sinica, 20, 1125-1142). The\nprobability models for the failure indicator and for the\nmissingness mechanism are estimated jointly by maximum\nlikelihood following Sun, Xie, and Liang (2013)\n<doi:10.1007/s11425-012-4492-x>, and a Nadaraya-Watson\nkernel-smoothed estimator of the missingness propensity is\nconstructed following Qiu, Chen, and Zhou (2015)\n<doi:10.1016/j.spl.2014.12.006>. Both an asymptotic\n(sandwich-type) variance estimator and a nonparametric\nbootstrap variance estimator are provided. When failure\nindicators are fully observed the estimators reduce\nalgebraically to the classical Cox partial-likelihood\nestimator.",
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