dlogpdtheta_wei

Produce partial derivatives of log(p(t)) the logarithm of the
probability to be cured

internal

Fits a variety of cure models using excess hazard modeling methodology such as the mixture model proposed by Phillips et al. (2002) <doi:10.1002/sim.1101> The Weibull distribution is used to represent the survival function of the uncured patients; Fits also non-mixture cure model such as the time-to-null excess hazard model proposed by Boussari et al. (2020) <doi:10.1111/biom.13361>.

Juste Goungounga

curesurv

Mixture and Non Mixture Parametric Cure Models to Estimate Cure
Indicators

Judith Breaud

Olayide Boussari

Laura Botta 

Valerie Jooste

dlogpdtheta_wei function

<dl><dt>z_pcured</dt>
<dd>covariates matrix acting on cure proportion</dd>
<dt>z_ucured</dt>
<dd>covariates matrix acting on survival function of uncured</dd>
<dt>x</dt>
<dd>time at which the estimates are predicted</dd>
<dt>theta</dt>
<dd>estimated parameters of the cumulative excess hazard from a mixture
model using curesurv and uncured survival following a Weibull distribution</dd>
<dt>cumLexctopred</dt>
<dd>a pre-prediction parameter obtained with cumLexc_alphaweibull_topred</dd></dl>

Arguments

dlogpdtheta_wei function — dlogpdtheta_wei

<dl>

<dt>z_pcured</dt>
<dd>covariates matrix acting on cure proportion</dd>


<dt>z_ucured</dt>
<dd>covariates matrix acting on survival function of uncured</dd>


<dt>x</dt>
<dd>time at which the estimates are predicted</dd>


<dt>theta</dt>
<dd>estimated parameters of the cumulative excess hazard from a mixture
model using curesurv and uncured survival following a Weibull distribution</dd>


<dt>cumLexctopred</dt>
<dd>a pre-prediction parameter obtained with cumLexc_alphaweibull_topred</dd>

</dl>

dlogpdtheta_wei: dlogpdtheta_wei function

Description

Usage

Arguments