Longfun

Change hazard and cumulative hazard to long format

Copula based Cox proportional hazards models for survival data subject to dependent
censoring. This approach does not assume that the parameter defining the copula is known. The
dependency parameter is estimated with other finite model parameters by maximizing a Pseudo
likelihood function. The cumulative hazard function is estimated via estimating equations
derived based on martingale ideas. Available copula functions include Frank, Gumbel and Normal
copulas. Only Weibull and lognormal models are allowed for the censoring model, even though any
parametric model that satisfies certain identifiability conditions could be used. Implemented
methods are described in the article "Copula based Cox proportional hazards models for dependent
censoring" by Deresa and Van Keilegom (2024) <doi:10.1080/01621459.2022.2161387>.

Negera Wakgari Deresa

SemiPar.depCens

Copula Based Cox Proportional Hazards Models for Dependent
Censoring

Ingrid Van Keilegom

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>lhat</dt>
<dd>Hazard function estimate</dd>
<dt>Lhat</dt>
<dd>Cumulative hazard function estimate</dd></dl>

Arguments

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>lhat</dt>
<dd>Hazard function estimate</dd>


<dt>Lhat</dt>
<dd>Cumulative hazard function estimate</dd>

</dl>

Longfun: Long format

Description

Usage

Arguments