print.IDEMFIT

Print method of the class <code>IDEMFIT</code> generated by
<code>imFitModel</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.IDEMFIT function

<dl><dt>x</dt>
<dd>A class <code>IDEMFIT</code> object generated by <code>imFitModel</code></dd>
<dt>...</dt>
<dd>Additional arguments</dd></dl>

Arguments

Print model fitting results — print.IDEMFIT

<dl>

<dt>x</dt>
<dd>A class <code>IDEMFIT</code> object generated by <code>imFitModel</code></dd>


<dt>...</dt>
<dd>Additional arguments</dd>

</dl>

print.IDEMFIT: Print model fitting results

Description

Usage

Arguments

Details

See Also