boot.nonparTrans

This function estimates the bootstrap standard errors for the finite-dimensional model parameters and for the non-parametric transformation
function. Parallel computing using foreach has been used to speed up the estimation of standard errors.

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

boot.nonparTrans function

<dl><dt>init</dt>
<dd>Initial values for the finite dimensional parameters obtained from the fit of <code>NonParTrans</code></dd>
<dt>resData</dt>
<dd>Data matrix with three columns; Z = the observed survival time, d1 = the censoring indicator of T
and d2 = the censoring indicator of C.</dd>
<dt>X</dt>
<dd>Data matrix with covariates related to T</dd>
<dt>W</dt>
<dd>Data matrix with covariates related to C.</dd>
<dt>n.boot</dt>
<dd>Number of bootstraps to use in the estimation of bootstrap standard errors.</dd>
<dt>n.iter</dt>
<dd>Number of iterations; the default is <code>n.iter = 15</code>. The larger the number of iterations, the longer the computational time.</dd>
<dt>eps</dt>
<dd>Convergence error. This is set by the user in such away that the desired convergence is met; the default is <code>eps = 1e-3</code></dd></dl>

Arguments

Nonparametric bootstrap approach for a Semiparametric transformation model under dependent censpring — boot.nonparTrans

<dl>

<dt>init</dt>
<dd>Initial values for the finite dimensional parameters obtained from the fit of <code>NonParTrans</code></dd>


<dt>resData</dt>
<dd>Data matrix with three columns; Z = the observed survival time, d1 = the censoring indicator of T
and d2 = the censoring indicator of C.</dd>


<dt>X</dt>
<dd>Data matrix with covariates related to T</dd>


<dt>W</dt>
<dd>Data matrix with covariates related to C.</dd>


<dt>n.boot</dt>
<dd>Number of bootstraps to use in the estimation of bootstrap standard errors.</dd>


<dt>n.iter</dt>
<dd>Number of iterations; the default is <code>n.iter = 15</code>. The larger the number of iterations, the longer the computational time.</dd>


<dt>eps</dt>
<dd>Convergence error. This is set by the user in such away that the desired convergence is met; the default is <code>eps = 1e-3</code></dd>

</dl>

boot.nonparTrans: Nonparametric bootstrap approach for a Semiparametric transformation model under dependent censpring

Description

Usage

Value

Arguments