pool_bootstrap_normal

Get point estimate, confidence interval and p-value using
the normal approximation.

Implements standard and reference based multiple imputation methods for continuous
longitudinal endpoints (Gower-Page et al. (2022) <doi:10.21105/joss.04251>). In particular,
this package supports deterministic conditional mean imputation and jackknifing as described
in Wolbers et al. (2022) <doi:10.1002/pst.2234>, Bayesian multiple imputation as described
in Carpenter et al. (2013) <doi:10.1080/10543406.2013.834911>, and bootstrapped maximum
likelihood imputation as described in von Hippel and Bartlett (2021) <doi: 10.1214/20-STS793>.

Isaac Gravestock

rbmi

Reference Based Multiple Imputation

Craig Gower-Page

Alessandro Noci

Marcel Wolbers

Daniel Sabanes Bove

F. Hoffmann-La Roche AG 

pool_bootstrap_normal function

<dl><dt>est</dt>
<dd>a numeric vector of point estimates from each bootstrap sample.</dd>
<dt>conf.level</dt>
<dd>confidence level of the returned confidence interval.
Must be a single number between 0 and 1. Default is 0.95.</dd>
<dt>alternative</dt>
<dd>a character string specifying the alternative hypothesis,
must be one of <code>"two.sided"</code> (default), <code>"greater"</code> or <code>"less"</code>.</dd></dl>

Arguments

Bootstrap Pooling via normal approximation — pool_bootstrap_normal

<dl>

<dt>est</dt>
<dd>a numeric vector of point estimates from each bootstrap sample.</dd>


<dt>conf.level</dt>
<dd>confidence level of the returned confidence interval.
Must be a single number between 0 and 1. Default is 0.95.</dd>


<dt>alternative</dt>
<dd>a character string specifying the alternative hypothesis,
must be one of <code>"two.sided"</code> (default), <code>"greater"</code> or <code>"less"</code>.</dd>

</dl>

pool_bootstrap_normal: Bootstrap Pooling via normal approximation

Description

Usage

Arguments

Details