summary.AUCjmcs

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.

Shanpeng Li

FastJM

Semi-Parametric Joint Modeling of Longitudinal and Survival Data

Ning Li

Hong Wang

Jin Zhou

Hua Zhou

Gang Li

summary.AUCjmcs function

<dl><dt>object</dt>
<dd>object of class 'AUCjmcs'.</dd>
<dt>digits</dt>
<dd>number of digits of decimal to be printed.</dd>
<dt>...</dt>
<dd>Further arguments passed to or from other methods.</dd></dl>

Arguments

Shanpeng Li <a href="/link/lishanpeng0913%40ucla.edu?package=FastJM&version=1.4.2" data-mini-rdoc="FastJM::lishanpeng0913@ucla.edu">lishanpeng0913@ucla.edu</a>

Author

Print AUCjmcs — summary.AUCjmcs

<dl>

<dt>object</dt>
<dd>object of class 'AUCjmcs'.</dd>


<dt>digits</dt>
<dd>number of digits of decimal to be printed.</dd>


<dt>...</dt>
<dd>Further arguments passed to or from other methods.</dd>

</dl>

Shanpeng Li <a href='mailto:lishanpeng0913@ucla.edu'>lishanpeng0913@ucla.edu</a>

summary.AUCjmcs: Print AUCjmcs

Description

Usage

Value

Arguments

Author

See Also