extract_data_nmar_as_na

Set to NA outcome values that would be MNAR if they were missing
(i.e. which occur after an ICE handled using a reference-based imputation strategy)

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 

extract_data_nmar_as_na function

<dl><dt>longdata</dt>
<dd>R6 <code>longdata</code> object containing all relevant input data information.</dd></dl>

Arguments

Set to NA outcome values that would be MNAR if they were missing
(i.e. which occur after an ICE handled using a reference-based imputation strategy) — extract_data_nmar_as_na

<dl>

<dt>longdata</dt>
<dd>R6 <code>longdata</code> object containing all relevant input data information.</dd>

</dl>

extract_data_nmar_as_na: Set to NA outcome values that would be MNAR if they were missing (i.e. which occur after an ICE handled using a reference-based imputation strategy)

Description

Usage

Value

Arguments