get_ests_bmlmi

Compute pooled point estimates, standard error and degrees of freedom
according to the Von Hippel and Bartlett formula for Bootstrapped Maximum Likelihood
Multiple Imputation (BMLMI).

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 

get_ests_bmlmi function

<dl><dt>ests</dt>
<dd>numeric vector containing estimates from the analysis of the imputed datasets.</dd>
<dt>D</dt>
<dd>numeric representing the number of imputations between each bootstrap sample in the BMLMI method.</dd></dl>

Arguments

Von Hippel and Bartlett pooling of BMLMI method — get_ests_bmlmi

<dl>

<dt>ests</dt>
<dd>numeric vector containing estimates from the analysis of the imputed datasets.</dd>


<dt>D</dt>
<dd>numeric representing the number of imputations between each bootstrap sample in the BMLMI method.</dd>

</dl>

get_ests_bmlmi: Von Hippel and Bartlett pooling of BMLMI method

Description

Usage

Value

Arguments

Details

References