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).

Joint Modelling for Meta-Analytic (Multi-Study) Data
c(
person("Maria", "Sudell", email = "mesudell@liverpool.ac.uk",
role = c("cre", "aut"),
comment = c(ORCID = "0000-0002-7919-4981")),
person("Ruwanthi", "Kolamunnage-Dona", email = "kdrr@liverpool.ac.uk",
role = "aut",
comment = c(ORCID = "0000-0003-3886-6208")),
person("Catrin", "Tudur Smith", email = "cat1@liverpool.ac.uk",
role = "aut"))
Fits joint models of the type proposed by Henderson and colleagues
(2000) <doi:10.1093/biostatistics/1.4.465>, but extends to the multi-study,
meta-analytic case. Functions for meta-analysis of a single longitudinal and
a single  time-to-event outcome from multiple studies using joint models.
Options to produce plots for multi study joint data, to pool joint model
fits from 'JM' and 'joineR' packages in a two stage meta-analysis, and to
model multi-study joint data in a one stage meta-analysis.
ggplot2, grid, gridExtra, gtools, joineR, MASS, meta, msm,
nlme, statmod, stats, Matrix, utils
Maria Sudell [cre, aut] (<https://orcid.org/0000-0002-7919-4981>),
Ruwanthi Kolamunnage-Dona [aut]
(<https://orcid.org/0000-0003-3886-6208>),
Catrin Tudur Smith [aut]

ggplot2, grid, gridExtra, gtools, joineR, MASS, meta, msm,
statmod, stats, Matrix, utils

Implements high-dimensional multivariate regression by stacked generalisation (Wolpert 1992 <doi:10.1016/S0893-6080(05)80023-1>). For positively correlated outcomes, a single multivariate regression is typically more predictive than multiple univariate regressions. Includes functions for model fitting, extracting coefficients, outcome prediction, and performance measurement.