simClaytonOakesWei

Simulate observations from the Clayton-Oakes copula model with
Weibull type baseline and Cox marginals.

Implementation of various statistical models for multivariate
event history data <doi:10.1007/s10985-013-9244-x>. Including multivariate
cumulative incidence models <doi:10.1002/sim.6016>, and bivariate random
effects probit models (Liability models) <doi:10.1016/j.csda.2015.01.014>.
Modern methods for survival analysis, including regression modelling (Cox, Fine-Gray,
Ghosh-Lin, Binomial regression) with fast computation of influence functions.

Klaus Holst

mets

Analysis of Multivariate Event Times

Klaus K. Holst

Thomas Scheike 

simClaytonOakesWei function

<dl><dt>K</dt>
<dd>Number of clusters</dd>
<dt>n</dt>
<dd>Number of observations in each cluster</dd>
<dt>eta</dt>
<dd>1/variance</dd>
<dt>beta</dt>
<dd>Effect (log hazard ratio) of covariate</dd>
<dt>stoptime</dt>
<dd>Stopping time</dd>
<dt>weiscale</dt>
<dd>weibull scale parameter</dd>
<dt>weishape</dt>
<dd>weibull shape parameter</dd>
<dt>left</dt>
<dd>Left truncation</dd>
<dt>pairleft</dt>
<dd>pairwise (1) left truncation or individual (0)</dd></dl>

Arguments

Author

Simulate from the Clayton-Oakes frailty model — simClaytonOakesWei

<dl>

<dt>K</dt>
<dd>Number of clusters</dd>


<dt>n</dt>
<dd>Number of observations in each cluster</dd>


<dt>eta</dt>
<dd>1/variance</dd>


<dt>beta</dt>
<dd>Effect (log hazard ratio) of covariate</dd>


<dt>stoptime</dt>
<dd>Stopping time</dd>


<dt>weiscale</dt>
<dd>weibull scale parameter</dd>


<dt>weishape</dt>
<dd>weibull shape parameter</dd>


<dt>left</dt>
<dd>Left truncation</dd>


<dt>pairleft</dt>
<dd>pairwise (1) left truncation or individual (0)</dd>

</dl>

simClaytonOakesWei: Simulate from the Clayton-Oakes frailty model

Description

Usage

Arguments

Author