dD.hat

This function computes the matrix of sample derivatives of the
sample variances.

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

dD.hat function

<dl><dt>data</dt>
<dd>Data frame.</dd>
<dt>beta</dt>
<dd>Vector of coefficients.</dd>
<dt>t</dt>
<dd>Time point at which to evaluate the (derivatives of) the moment
functions. Also allowed to
be a vector of time points (used in estimating the model under assumed time-
independent coefficients).</dd>
<dt>hp</dt>
<dd>List of hyperparamerers.</dd>
<dt>mi.mat</dt>
<dd>A precomputed matrix of moment function evaluations at each
observation. If supplied, some computations can be skipped. Default is
<code>mi.mat = NULL</code>.</dd>
<dt>m.avg</dt>
<dd>A precomputed vector of the sample average of the moment
functions. If not supplied, this vector is computed. Default is
<code>m.avg = NULL</code>.</dd>
<dt>dm.avg</dt>
<dd>Matrix of precomputed sample averages of the derivatives of the
moment functions. Default is <code>dm.avg = NULL</code>.</dd>
<dt>dmi.tens</dt>
<dd>3D tensor of precomputed evaluations of the derivatives of
the moment functions. Default is <code>dmi.tens = NULL</code>.</dd></dl>

Arguments

Obtain the matrix of partial derivatives of the sample variances. — dD.hat

<dl>

<dt>data</dt>
<dd>Data frame.</dd>


<dt>beta</dt>
<dd>Vector of coefficients.</dd>


<dt>t</dt>
<dd>Time point at which to evaluate the (derivatives of) the moment
functions. Also allowed to
be a vector of time points (used in estimating the model under assumed time-
independent coefficients).</dd>


<dt>hp</dt>
<dd>List of hyperparamerers.</dd>


<dt>mi.mat</dt>
<dd>A precomputed matrix of moment function evaluations at each
observation. If supplied, some computations can be skipped. Default is
<code>mi.mat = NULL</code>.</dd>


<dt>m.avg</dt>
<dd>A precomputed vector of the sample average of the moment
functions. If not supplied, this vector is computed. Default is
<code>m.avg = NULL</code>.</dd>


<dt>dm.avg</dt>
<dd>Matrix of precomputed sample averages of the derivatives of the
moment functions. Default is <code>dm.avg = NULL</code>.</dd>


<dt>dmi.tens</dt>
<dd>3D tensor of precomputed evaluations of the derivatives of
the moment functions. Default is <code>dmi.tens = NULL</code>.</dd>

</dl>

dD.hat: Obtain the matrix of partial derivatives of the sample variances.

Description

Usage

Value

Arguments