likF.cmprsk.Cholesky

This function parametrizes the covariance matrix using its
Cholesky decomposition, so that optimization of the likelihood can be done
based on this parametrization, and positive-definiteness of the covariance
matrix is guaranteed at each step of the optimization algorithm.

Several statistical methods for analyzing survival data under various forms of dependent
censoring are implemented in the package. In addition to accounting for dependent censoring, it
offers tools to adjust for unmeasured confounding factors. The implemented approaches allow
users to estimate the dependency between survival time and dependent censoring time, based
solely on observed survival data. For more details on the methods, refer to Deresa and Van
Keilegom (2021) <doi:10.1093/biomet/asaa095>, Czado and Van Keilegom (2023)
<doi:10.1093/biomet/asac067>, Crommen et al. (2024) <doi:10.1007/s11749-023-00903-9>,
Deresa and Van Keilegom (2024) <doi:10.1080/01621459.2022.2161387>, Rutten et al. (2024+)
<doi:10.48550/arXiv.2403.11860> and Ding and Van Keilegom (2024).

Negera Wakgari Deresa

depCensoring

Statistical Methods for Survival Data with Dependent Censoring

Ilias Willems

Gilles Crommen

Jie Ding

Claudia Czado

Ingrid Van Keilegom

likF.cmprsk.Cholesky function

<dl><dt>par.chol</dt>
<dd>Vector of all second step model parameters, consisting of the
regression parameters, Cholesky decomposition of the variance-covariance
matrix elements and transformation parameters.</dd>
<dt>data</dt>
<dd>Data frame resulting from the 'uniformize.data.R' function.</dd>
<dt>admin</dt>
<dd>Boolean value indicating whether the data contains
administrative censoring.</dd>
<dt>conf</dt>
<dd>Boolean value indicating whether the data contains confounding
and hence indicating the presence of Z and W.</dd>
<dt>cf</dt>
<dd>"Control function" to be used. This can either be the (i) estimated
control function, (ii) the true control function, (iii) the instrumental
variable, or (iv) nothing (<code>cf = NULL</code>). Option (ii) is used when
comparing the two-step estimator to the oracle estimator, and option (iii) is
used to compare the two-step estimator with the naive estimator.</dd>
<dt>eps</dt>
<dd>Minimum value for the diagonal elements in the covariance matrix.
Default is <code>eps = 0.001</code>.</dd></dl>

Arguments

Wrapper implementing likelihood function using Cholesky factorization. — likF.cmprsk.Cholesky

<dl>

<dt>par.chol</dt>
<dd>Vector of all second step model parameters, consisting of the
regression parameters, Cholesky decomposition of the variance-covariance
matrix elements and transformation parameters.</dd>


<dt>data</dt>
<dd>Data frame resulting from the 'uniformize.data.R' function.</dd>


<dt>admin</dt>
<dd>Boolean value indicating whether the data contains
administrative censoring.</dd>


<dt>conf</dt>
<dd>Boolean value indicating whether the data contains confounding
and hence indicating the presence of Z and W.</dd>


<dt>cf</dt>
<dd>"Control function" to be used. This can either be the (i) estimated
control function, (ii) the true control function, (iii) the instrumental
variable, or (iv) nothing (<code>cf = NULL</code>). Option (ii) is used when
comparing the two-step estimator to the oracle estimator, and option (iii) is
used to compare the two-step estimator with the naive estimator.</dd>


<dt>eps</dt>
<dd>Minimum value for the diagonal elements in the covariance matrix.
Default is <code>eps = 0.001</code>.</dd>

</dl>

likF.cmprsk.Cholesky: Wrapper implementing likelihood function using Cholesky factorization.

Description

Usage

Value

Arguments