Semi-Parametric Joint Modeling of Longitudinal and Survival Data
Maximum likelihood estimation for the semi-parametric joint modeling of competing risks and longitudinal data
applying customized linear scan algorithms, proposed by Li and colleagues (2021) <arXiv:2110.14822>. The time-to-event data is
modelled using a (cause-specific) Cox proportional hazards regression model with time-fixed covariates. The longitudinal
outcome is modelled using a linear mixed effects model. The association is captured by shared random effects. The model
is estimated using an Expectation Maximization algorithm.

Maximum likelihood estimation for the semi-parametric joint modeling of competing risks and longitudinal data
applying customized linear scan algorithms, proposed by Li and colleagues (2022) <doi:10.1155/2022/1362913>. The time-to-event data is
modelled using a (cause-specific) Cox proportional hazards regression model with time-fixed covariates. The longitudinal
outcome is modelled using a linear mixed effects model. The association is captured by shared random effects. The model
is estimated using an Expectation Maximization algorithm.
Rcpp (>= 1.0.7), survival, dplyr, nlme, mvtnorm

Rcpp (>= 1.0.7), survival, dplyr, nlme, mvtnorm, Hmisc

A Fast Multiple-Kernel Method Based on a Low-Rank Approximation
Rachel Marceau, Wenbin Lu, Michele M. Sale, Bradford B. Worrall, Stephen R. Williams, Fang-Chi Hsu, Jung-Ying Tzeng, and Shannon T. Holloway
A computationally efficient and statistically rigorous fast Kernel Machine method for multi-kernel analysis. The approach is based on a low-rank approximation to the nuisance effect kernel matrices. The algorithm is applicable to continuous, binary, and survival traits and is implemented using the existing single-kernel analysis software 'SKAT' and 'coxKM'. 'coxKM' can be obtained from http://www.hsph.harvard.edu/xlin/software.html.

Rachel Marceau, Wenbin Lu, Michele M. Sale, Bradford B. Worrall,
Stephen R. Williams, Fang-Chi Hsu, Jung-Ying Tzeng, and Shannon T. Holloway
A computationally efficient and statistically rigorous fast
Kernel Machine method for multi-kernel analysis. The approach is based on
a low-rank approximation to the nuisance effect kernel matrices. The
algorithm is applicable to continuous, binary, and survival traits and
is implemented using the existing single-kernel analysis software 'SKAT'
and 'coxKM'. 'coxKM' can be obtained from
<https://github.com/lin-lab/coxKM>.