AUCjmcs

Time-dependent AUC for joint models

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

AUCjmcs function

<dl><dt>seed</dt>
<dd>a numeric value of seed to be specified for cross validation.</dd>
<dt>object</dt>
<dd>object of class 'jmcs'.</dd>
<dt>landmark.time</dt>
<dd>a numeric value of time for which dynamic prediction starts..</dd>
<dt>horizon.time</dt>
<dd>a numeric vector of future times for which predicted probabilities are to be computed.</dd>
<dt>obs.time</dt>
<dd>a character string of specifying a longitudinal time variable.</dd>
<dt>method</dt>
<dd>estimation method for predicted probabilities. If <code>Laplace</code>, then the empirical empirical
estimates of random effects is used. If <code>GH</code>, then the pseudo-adaptive Gauss-Hermite quadrature is used.</dd>
<dt>quadpoint</dt>
<dd>the number of pseudo-adaptive Gauss-Hermite quadrature points if <code>method = "GH"</code>.</dd>
<dt>maxiter</dt>
<dd>the maximum number of iterations of the EM algorithm that the 
function will perform. Default is 10000.</dd>
<dt>n.cv</dt>
<dd>number of folds for cross validation. Default is 3.</dd>
<dt>survinitial</dt>
<dd>Fit a Cox model to obtain initial values of the parameter estimates. Default is TRUE.</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

Time-dependent AUC  for joint models — AUCjmcs

<dl>

<dt>seed</dt>
<dd>a numeric value of seed to be specified for cross validation.</dd>


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


<dt>landmark.time</dt>
<dd>a numeric value of time for which dynamic prediction starts..</dd>


<dt>horizon.time</dt>
<dd>a numeric vector of future times for which predicted probabilities are to be computed.</dd>


<dt>obs.time</dt>
<dd>a character string of specifying a longitudinal time variable.</dd>


<dt>method</dt>
<dd>estimation method for predicted probabilities. If <code>Laplace</code>, then the empirical empirical
estimates of random effects is used. If <code>GH</code>, then the pseudo-adaptive Gauss-Hermite quadrature is used.</dd>


<dt>quadpoint</dt>
<dd>the number of pseudo-adaptive Gauss-Hermite quadrature points if <code>method = "GH"</code>.</dd>


<dt>maxiter</dt>
<dd>the maximum number of iterations of the EM algorithm that the 
function will perform. Default is 10000.</dd>


<dt>n.cv</dt>
<dd>number of folds for cross validation. Default is 3.</dd>


<dt>survinitial</dt>
<dd>Fit a Cox model to obtain initial values of the parameter estimates. Default is TRUE.</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>

AUCjmcs: Time-dependent AUC for joint models

Description

Usage

Value

Arguments

Author

See Also