dat.sim.reg.comp.risks

This function generates competing risk data that can be used in
simulation studies.

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

dat.sim.reg.comp.risks function

<dl><dt>n</dt>
<dd>The sample size of the generated data set.</dd>
<dt>par</dt>
<dd>List of parameter vectors for each component of the transformation
model.</dd>
<dt>iseed</dt>
<dd>Random seed.</dd>
<dt>s</dt>
<dd>The number of competing risks. Note that the given parameter vector
could specify the parameters for the model with more than s competing risks,
but in this case only the first s sets of parameters will be considered.</dd>
<dt>conf</dt>
<dd>Boolean value indicating whether the data set should contain
confounding.</dd>
<dt>Zbin</dt>
<dd>Indicator whether the confounded variable is binary
<code>Zbin = 1</code> or not <code>Zbin = 0</code>. If <code>conf = FALSE</code>, this
variable is ignored.</dd>
<dt>Wbin</dt>
<dd>Indicator whether the instrument is binary (<code>Zbin = 1</code>) or
not <code>Zbin = 0</code>.</dd>
<dt>type.cov</dt>
<dd>Vector of characters "c" and "b", indicating which exogenous
covariates should be continuous <code>"c"</code> or binary <code>"b"</code>.</dd>
<dt>A.upper</dt>
<dd>The upper bound on the support of the administrative censoring
distribution. This can be used to control for the amount of administrative
censoring in the data. Default is <code>A.upper = 15</code>. <code>A.upper = NULL</code>
will return a data set without administrative censoring.</dd></dl>

Arguments

Data generation function for competing risks data — dat.sim.reg.comp.risks

<dl>

<dt>n</dt>
<dd>The sample size of the generated data set.</dd>


<dt>par</dt>
<dd>List of parameter vectors for each component of the transformation
model.</dd>


<dt>iseed</dt>
<dd>Random seed.</dd>


<dt>s</dt>
<dd>The number of competing risks. Note that the given parameter vector
could specify the parameters for the model with more than s competing risks,
but in this case only the first s sets of parameters will be considered.</dd>


<dt>conf</dt>
<dd>Boolean value indicating whether the data set should contain
confounding.</dd>


<dt>Zbin</dt>
<dd>Indicator whether the confounded variable is binary
<code>Zbin = 1</code> or not <code>Zbin = 0</code>. If <code>conf = FALSE</code>, this
variable is ignored.</dd>


<dt>Wbin</dt>
<dd>Indicator whether the instrument is binary (<code>Zbin = 1</code>) or
not <code>Zbin = 0</code>.</dd>


<dt>type.cov</dt>
<dd>Vector of characters "c" and "b", indicating which exogenous
covariates should be continuous <code>"c"</code> or binary <code>"b"</code>.</dd>


<dt>A.upper</dt>
<dd>The upper bound on the support of the administrative censoring
distribution. This can be used to control for the amount of administrative
censoring in the data. Default is <code>A.upper = 15</code>. <code>A.upper = NULL</code>
will return a data set without administrative censoring.</dd>

</dl>

dat.sim.reg.comp.risks: Data generation function for competing risks data

Description

Usage

Value

Arguments