ctsTrialWide

A dataset in the wide form containing simulated data with a repeatedly measured
outcome. Some outcome values are missing. The missing data pattern is
monotone. There are two baseline covariates.

datasets

Implements so called Maximum Likelihood Multiple Imputation as described by von Hippel and Bartlett (2021) <doi:10.1214/20-STS793>. A number of different imputations are available, by utilising the 'norm', 'cat' and 'mix' packages. Inferences can be performed either using combination rules similar to Rubin's or using a likelihood score based approach based on theory by Wang and Robins (1998) <doi:10.1093/biomet/85.4.935>.

Jonathan Bartlett

mlmi

Maximum Likelihood Multiple Imputation

ctsTrialWide function

A data frame with 500 rows and 7 variables:<dl>
 <dt>id</dt>
<dd>ID for individual</dd> <dt>trt</dt>
<dd>A numeric 0/1 variable indicating control or active treatment group</dd> <dt>v</dt>
<dd>A baseline covariate</dd> <dt>y0</dt>
<dd>Baseline measurement of the outcome variable</dd> <dt>y1</dt>
<dd>Outcome measurement at visit 1</dd> <dt>y2</dt>
<dd>Outcome measurement at visit 2</dd> <dt>y3</dt>
<dd>Outcome measurement at visit 3</dd>
</dl>

Format

Simulated example data with continuous outcome measured repeatedly over time — ctsTrialWide

A data frame with 500 rows and 7 variables:<dl>
 <dt>id</dt>
<dd>ID for individual</dd>

 <dt>trt</dt>
<dd>A numeric 0/1 variable indicating control or active treatment group</dd>

 <dt>v</dt>
<dd>A baseline covariate</dd>

 <dt>y0</dt>
<dd>Baseline measurement of the outcome variable</dd>

 <dt>y1</dt>
<dd>Outcome measurement at visit 1</dd>

 <dt>y2</dt>
<dd>Outcome measurement at visit 2</dd>

 <dt>y3</dt>
<dd>Outcome measurement at visit 3</dd>


</dl>

ctsTrialWide: Simulated example data with continuous outcome measured repeatedly over time

Description

Usage

Arguments

Format