simClaytonOakes

simClaytonOakesLam

Simulate observations from the Clayton-Oakes copula model with
piecewise constant 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>.
Also contains two-stage binomial modelling that can do pairwise odds-ratio
dependence modelling based marginal logistic regression models. This is an
alternative to the alternating logistic regression approach (ALR).

Klaus Holst

mets

Analysis of Multivariate Event Times

Klaus K. Holst

Thomas Scheike 

simClaytonOakes 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>variance</dd>
<dt>beta</dt>
<dd>Effect (log hazard ratio) of covariate</dd>
<dt>stoptime</dt>
<dd>Stopping time</dd>
<dt>lam</dt>
<dd>constant hazard</dd>
<dt>left</dt>
<dd>Left truncation</dd>
<dt>pairleft</dt>
<dd>pairwise (1) left truncation or individual (0)</dd>
<dt>trunc.prob</dt>
<dd>Truncation probability</dd>
<dt>same</dt>
<dd>if 1 then left-truncation is same also for univariate truncation</dd></dl>

Arguments

Author

Simulate from the Clayton-Oakes frailty model — simClaytonOakes

<dl>

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


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


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


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


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


<dt>lam</dt>
<dd>constant hazard</dd>


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


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


<dt>trunc.prob</dt>
<dd>Truncation probability</dd>


<dt>same</dt>
<dd>if 1 then left-truncation is same also for univariate truncation</dd>

</dl>

simClaytonOakes: Simulate from the Clayton-Oakes frailty model

Description

Usage

Arguments

Author