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  "Title": "Joint Model of Heterogeneous Repeated Measures and Survival Data",
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  "Date": "2026-03-29",
  "Authors@R": "c(\nperson(\"Shanpeng\", \"Li\", email = \"lishanpeng0913@ucla.edu\",\nrole = c(\"aut\", \"cre\")),\nperson(\"Gang\", \"Li\", email = \"vli@ucla.edu\",\nrole = \"ctb\")\n)",
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  "Description": "Maximum likelihood estimation for the semi-parametric\njoint modeling of competing risks and longitudinal data in the\npresence of heterogeneous within-subject variability, proposed\nby Li and colleagues (2023) <doi:10.48550/arXiv.2506.12741>.\nThe proposed method models the within-subject variability of\nthe biomarker and associates it with the risk of the competing\nrisks event. The time-to-event data is modeled using a\n(cause-specific) Cox proportional hazards regression model with\ntime-fixed covariates. The longitudinal outcome is modeled\nusing a mixed-effects location and scale model. The association\nis captured by shared random effects. The model is estimated\nusing an Expectation Maximization algorithm. This is the final\nrelease of the 'JMH' package. Active development has been moved\nto the 'FastJM' package, which provides improved functionality\nand ongoing support. Users are strongly encouraged to\ntransition to 'FastJM'.",
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