ex_compet

A dataset containing simulated competing risks data. There are two competing risks, and
some times are also censored.

datasets

Implements multiple imputation of missing covariates by
Substantive Model Compatible Fully Conditional Specification.
This is a modification of the popular FCS/chained equations
multiple imputation approach, and allows imputation of missing
covariate values from models which are compatible with the user
specified substantive model.

Jonathan Bartlett

smcfcs

Multiple Imputation of Covariates by Substantive Model
Compatible Fully Conditional Specification

Ruth Keogh

Edouard F. Bonneville

Claus Thorn Ekstrøm

ex_compet function

A data frame with 1000 rows and 4 variables:<dl>
 <dt>t</dt>
<dd>Time to event or censoring</dd> <dt>d</dt>
<dd>Indicator of whether event 1 occurred (d=1), event 2 occurred (d=2) or individual was censored (d=0)</dd> <dt>x1</dt>
<dd>Partially observed binary covariate, with linear effects on log competing risk hazards</dd> <dt>x2</dt>
<dd>Partially observed normally distributed (conditional on x1) covariate, with linear effects
 on log competing risk hazards</dd>
</dl>

Format

Simulated example data with competing risks outcome and partially observed covariates — ex_compet

A data frame with 1000 rows and 4 variables:<dl>
 <dt>t</dt>
<dd>Time to event or censoring</dd>

 <dt>d</dt>
<dd>Indicator of whether event 1 occurred (d=1), event 2 occurred (d=2) or individual was censored (d=0)</dd>

 <dt>x1</dt>
<dd>Partially observed binary covariate, with linear effects on log competing risk hazards</dd>

 <dt>x2</dt>
<dd>Partially observed normally distributed (conditional on x1) covariate, with linear effects
 on log competing risk hazards</dd>


</dl>

ex_compet: Simulated example data with competing risks outcome and partially observed covariates

Description

Usage

Arguments

Format