prediction_cf_methods

print.prediction_cf

S3 Methods for <code>prediction_cf</code>

internal

Performs robust estimation and inference when using covariate adjustment
and/or covariate-adaptive randomization in randomized controlled trials.
This package is trimmed to reduce the dependencies and validated to be used across industry.
See "FDA's final guidance on covariate adjustment"<https://www.regulations.gov/docket/FDA-2019-D-0934>, Tsiatis (2008) <doi:10.1002/sim.3113>,
Bugni et al. (2018) <doi:10.1080/01621459.2017.1375934>, Ye, Shao, Yi, and Zhao (2023)<doi:10.1080/01621459.2022.2049278>,
Ye, Shao, and Yi (2022)<doi:10.1093/biomet/asab015>, Rosenblum and van der Laan (2010)<doi:10.2202/1557-4679.1138>,
Wang et al. (2021)<doi:10.1080/01621459.2021.1981338>, Ye, Bannick, Yi, and Shao (2023)<doi:10.1080/24754269.2023.2205802>,
and Bannick, Shao, Liu, Du, Yi, and Ye (2024)<doi:10.48550/arXiv.2306.10213>.

Liming Li

RobinCar2

ROBust INference for Covariate Adjustment in Randomized Clinical
Trials

Marlena Bannick

Dong Xi

Ting Ye

Yanyao Yi

Gregory Chen

Gilead Sciences, Inc. 

F. Hoffmann-La Roche AG 

Merck Sharp & Dohme, Inc. 

AstraZeneca plc 

Eli Lilly and Company 

The University of Washington 

prediction_cf_methods function

<dl><dt>x</dt>
<dd>(<code>prediction_cf</code>) the obtained counter-factual prediction object.</dd>
<dt>level</dt>
<dd>(<code>number</code>) the significance level.</dd></dl>

Arguments

<ul>
<li><code>print(prediction_cf)</code>: prints the prediction_cf object.</li>
</ul>

Functions

S3 Methods for prediction_cf — prediction_cf_methods

<dl>

<dt>x</dt>
<dd>(<code>prediction_cf</code>) the obtained counter-factual prediction object.</dd>


<dt>level</dt>
<dd>(<code>number</code>) the significance level.</dd>

</dl>

S3 Methods for <code>prediction_cf</code>

prediction_cf_methods: S3 Methods for `prediction_cf`

Description

Usage

Value

Arguments

Functions