c(person("Viviane", "Philipps", role=c("aut", "cre"), email="Viviane.Philipps@u-bordeaux.fr"),
person("Tiphaine", "Saulnier", role="aut"),
person("Cecile", "Proust-Lima", role="aut"))
Estimation of extended joint models with shared random effects. Longitudinal data are handled in latent process models for continuous (Gaussian or curvilinear) and ordinal outcomes while proportional hazard models are used for the survival part. We propose a frequentist approach using maximum likelihood estimation. See Saulnier et al, 2021 <arXiv:2110.02612>.
R (>= 2.14.0), survival (>= 2.37-2), randtoolbox, stringr,
marqLevAlg (>= 2.0.6)
Viviane Philipps [aut, cre],
Tiphaine Saulnier [aut],
Cecile Proust-Lima [aut]

Joint Modelling of Longitudinal and Survival Data
Shared parameter models for the joint modelling of longitudinal and time-to-event data.
R(>= 2.5.0), MASS, nlme, splines, survival

R (>= 2.5.0), MASS, nlme, splines, survival

Shared parameter models for the joint modelling of
longitudinal and time-to-event data.
R (>= 2.7.0), MASS, nlme, splines, survival

R (>= 2.9.0), MASS, nlme, splines, survival

R (>= 2.10.0), MASS, nlme, splines, survival

R (>= 2.11.0), MASS, nlme, splines, survival

Joint Modeling of Longitudinal and Survival Data
Shared parameter models for the joint modeling of
longitudinal and time-to-event data.

Shared parameter models for the joint modeling of longitudinal and time-to-event data.

R (>= 2.12.0), MASS, nlme, splines, survival

R (>= 2.14.0), MASS, nlme, splines, survival

R (>= 3.0.0), MASS, nlme, splines, survival

Zeng Xianli <a0123862@u.nus.edu>, Hang Weiqiang <e0010758@u.nus.edu>
Computes the Jackknife Mutual Information (JMI) between two random vectors and provides the p-value for dependence tests. See Zeng, X., Xia, Y. and Tong, H. (2018) <doi:10.1073/pnas.1715593115>.

Joint Modeling of Longitudinal and Survival Data - Power
Calculation
Emil A. Cornea, Liddy M. Chen, Bahjat F. Qaqish, Haitao Chu,
and Joseph G. Ibrahim
The package contains a R-function to perform power
calculation for joint modeling of longitudinal and survival
data, with k-th order trajectories, when the
variance-covariance matrix Sigma_theta is unknown.