FastJM

Description

Implements scalable joint models for large-scale competing risks time-to-event data with one or multiple longitudinal biomarkers using the efficient algorithms developed by Li et al. (2022) <doi:10.1155/2022/1362913> and <doi:10.48550/arXiv.2506.12741>. The time-to-event process is modeled using a cause-specific Cox proportional hazards model with time-fixed covariates, while longitudinal biomarkers are modeled using linear mixed-effects models. The association between the longitudinal and survival processes is captured through shared random effects. The package enables analysis of large-scale biomedical data to model biomarker trajectories, estimate their effects on event risks, and perform dynamic prediction of future events based on patients' longitudinal histories. Functions for simulating survival and longitudinal data for multiple biomarkers are included, along with built-in example datasets. The package also supports modeling a single biomarker with heterogeneous within-subject variability via functionality adapted from the 'JMH' package.

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installed size is 83.8Mb
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installed size is 83.8Mb
  sub-directories of 1Mb or more:
    libs  83.1Mb

installed size is 85.4Mb
  sub-directories of 1Mb or more:
    libs  84.7Mb

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