{ "_id": "6ad6b5660d12c08e591b7c0a", "Package": "FastJM", "Type": "Package", "Title": "Semi-Parametric Joint Modeling of Longitudinal and Survival Data", "Version": "1.7.2", "Date": "2026-10-20", "Authors@R": "c(\nperson(\"Shanpeng\", \"Li\", email = \"lishanpeng0913@ucla.edu\",\nrole = c(\"aut\", \"cre\")),\nperson(\"Ace\", \"Mejia-Sanchez\", email = \"aceisabel.mejiasanchez1@ucr.edu\",\nrole = \"ctb\"),\nperson(\"Emily\", \"Ouyang\", email = \"eouya001@ucr.edu\",\nrole = \"ctb\"),\nperson(\"Xinping\", \"Cui\", email = \"xinping.cui@ucr.edu\",\nrole = \"ctb\"),\nperson(\"Gang\", \"Li\", email = \"vli@ucla.edu\",\nrole = \"ctb\")\n)", "Maintainer": "Shanpeng Li ", "Encoding": "UTF-8", "Description": "Implements scalable joint models for large-scale competing\nrisks time-to-event data with one or multiple longitudinal\nbiomarkers using the efficient algorithms developed by Li et\nal. (2022) and\n. The time-to-event process is modeled\nusing a cause-specific Cox proportional hazards model with\ntime-fixed covariates, while longitudinal biomarkers are\nmodeled using linear mixed-effects models. The association\nbetween the longitudinal and survival processes is captured\nthrough shared random effects. The package enables analysis of\nlarge-scale biomedical data to model biomarker trajectories,\nestimate their effects on event risks, and perform dynamic\nprediction of future events based on patients' longitudinal\nhistories. Functions for simulating survival and longitudinal\ndata for multiple biomarkers are included, along with built-in\nexample datasets. 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