ex_flexsurv

A dataset containing simulated data where the time-to-event outcome is Weibull
distributed with two covariates, one of which is partially observed.

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_flexsurv function

A data frame with 1000 rows and 4 variables:<dl>
 <dt>t</dt>
<dd>Time to event (d=1) or censoring (d=0)</dd> <dt>d</dt>
<dd>Event indicator</dd> <dt>x</dt>
<dd>Partially observed binary covariate</dd> <dt>z</dt>
<dd>Fully observed continuous covariate</dd>
</dl>

Format

Simulated example data with time-to-event Weibull outcome and two covariates — ex_flexsurv

A data frame with 1000 rows and 4 variables:<dl>
 <dt>t</dt>
<dd>Time to event (d=1) or censoring (d=0)</dd>

 <dt>d</dt>
<dd>Event indicator</dd>

 <dt>x</dt>
<dd>Partially observed binary covariate</dd>

 <dt>z</dt>
<dd>Fully observed continuous covariate</dd>


</dl>

ex_flexsurv: Simulated example data with time-to-event Weibull outcome and two covariates

Description

Usage

Arguments

Format