_            _    _        _         _
      /\ \         /\ \ /\ \     /\_\      / /\
      \_\ \       /  \ \\ \ \   / / /     / /  \
      /\__ \     / /\ \ \\ \ \_/ / /     / / /\ \__
     / /_ \ \   / / /\ \ \\ \___/ /     / / /\ \___\
    / / /\ \ \ / / /  \ \_\\ \ \_/      \ \ \ \/___/
   / / /  \/_// / /   / / / \ \ \        \ \ \
  / / /      / / /   / / /   \ \ \   _    \ \ \
 / / /      / / /___/ / /     \ \ \ /_/\__/ / /
/_/ /      / / /____\/ /       \ \_\\ \/___/ /
\_\/       \/_________/         \/_/ \_____\/
r-joinerml 0.4.7
Propagated dependencies: r-tibble@3.2.1 r-survival@3.8-3 r-rcpparmadillo@14.4.2-1 r-rcpp@1.0.14 r-randtoolbox@2.0.5 r-nlme@3.1-168 r-mvtnorm@1.3-3 r-matrix@1.7-3 r-mass@7.3-65 r-lme4@1.1-37 r-ggplot2@3.5.2 r-generics@0.1.4 r-foreach@1.5.2 r-doparallel@1.0.17 r-cobs@1.3-9
Channel: guix-cran
Location: guix-cran/packages/j.scm (guix-cran packages j)
Home page: https://github.com/graemeleehickey/joineRML
Licenses: GPL 3 FSDG-compatible
Synopsis: Joint Modelling of Multivariate Longitudinal Data and Time-to-Event Outcomes
Description:

Fits the joint model proposed by Henderson and colleagues (2000) <doi:10.1093/biostatistics/1.4.465>, but extended to the case of multiple continuous longitudinal measures. The time-to-event data is modelled using a Cox proportional hazards regression model with time-varying covariates. The multiple longitudinal outcomes are modelled using a multivariate version of the Laird and Ware linear mixed model. The association is captured by a multivariate latent Gaussian process. The model is estimated using a Monte Carlo Expectation Maximization algorithm. This project was funded by the Medical Research Council (Grant number MR/M013227/1).

Total results: 1