Longfun

Change a nonparametric transformation function to long format

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>
and Willems et al. (2024+) <doi:10.48550/arXiv.2403.11860>.

Negera Wakgari Deresa

depCensoring

Statistical Methods for Survival Data with Dependent Censoring

Ilias Willems

Gilles Crommen

Ingrid Van Keilegom

Claudia Czado

Longfun function

<dl><dt>Z</dt>
<dd>Observed survival time, which is the minimum of T, C and A, where A is the administrative censoring time.</dd>
<dt>T1</dt>
<dd>Distinct observed survival time</dd>
<dt>H</dt>
<dd>Nonparametric transformation function estimate</dd></dl>

Arguments

Change H to long format — Longfun

<dl>

<dt>Z</dt>
<dd>Observed survival time, which is the minimum of T, C and A, where A is the administrative censoring time.</dd>


<dt>T1</dt>
<dd>Distinct observed survival time</dd>


<dt>H</dt>
<dd>Nonparametric transformation function estimate</dd>

</dl>

Longfun: Change H to long format

Description

Usage

Arguments