print.IDEMIMP

Print method for class <code>IDEMIMP</code> objects generated
by <code>imImpAll</code>

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

print.IDEMIMP function

<dl><dt>x</dt>
<dd>A class <code>IDEMIMP</code> object returned from <code>imImpAll</code></dd>
<dt>...</dt>
<dd>Extra arguments</dd></dl>

Arguments

Print imputation results — print.IDEMIMP

<dl>

<dt>x</dt>
<dd>A class <code>IDEMIMP</code> object returned from <code>imImpAll</code></dd>


<dt>...</dt>
<dd>Extra arguments</dd>

</dl>

print.IDEMIMP: Print imputation results

Description

Usage

Arguments

See Also