The Awakening and Breathing Controlled (ABC) trial randomized critically ill
patients receiving mechanical ventilation 1:1 within each study site to
management with a paired sedation plus ventilator weaning protocol involving
daily interruption of sedative through spontaneous awakening trials (SATs)
and spontaneous breathing trials (SBTs) or sedation per usual care
(UC) and SBTs.
The example dataset is from a single site substudy in ABC. The researchers
assessed differences in cognitive, psychological and functional outcomes at 3
and 12 months after randomization.

In randomized studies involving severely ill patients, functional
outcomes are often unobserved due to missed clinic visits, premature
withdrawal or death. It is well known that if these unobserved functional
outcomes are not handled properly, biased treatment comparisons can be
produced. In this package, we implement a procedure for comparing treatments
that is based on the composite endpoint of both the functional outcome and
survival. The procedure was proposed in Wang et al. (2016) <DOI:10.1111/biom.12594>
and Wang et al. (2020) <DOI:10.18637/jss.v093.i12>. It considers missing data
imputation with different sensitivity
analysis strategies to handle the unobserved functional outcomes not due to
death.

Chenguang Wang

idem

Inference in Randomized Controlled Trials with Death and
Missingness

abc function

A dataframe with 5 variables:<dl>
 <dt>AGE</dt>
<dd>Age</dd> <dt>TRT</dt>
<dd>Treatment assignment. 0: UC + SBT, 1: SAT + SBT</dd> <dt>SURV</dt>
<dd>Survival days</dd> <dt>Y2</dt>
<dd>Cognitive score at 12 months</dd> <dt>Y1</dt>
<dd>Cognitive score at 3 months</dd>
</dl>

Format

Example dataset — abc

A dataframe with 5 variables:<dl>
 <dt>AGE</dt>
<dd>Age</dd>

 <dt>TRT</dt>
<dd>Treatment assignment. 0: UC + SBT, 1: SAT + SBT</dd>

 <dt>SURV</dt>
<dd>Survival days</dd>

 <dt>Y2</dt>
<dd>Cognitive score at 12 months</dd>

 <dt>Y1</dt>
<dd>Cognitive score at 3 months</dd>


</dl>

abc: Example dataset

Description

Arguments

Format

References