d_lagscale

Calculates a delta value based upon a baseline delta value and a
post ICE scaling coefficient.

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 

d_lagscale function

<dl><dt>delta</dt>
<dd>a numeric vector. Determines the baseline amount of delta
to be applied to each visit.</dd>
<dt>dlag</dt>
<dd>a numeric vector. Determines the scaling to be applied
to <code>delta</code> based upon with visit the ICE occurred on. Must be the same
length as delta.</dd>
<dt>is_post_ice</dt>
<dd>logical vector. Indicates whether a visit is "post-ICE" or
not.</dd></dl>

Arguments

Calculate delta from a lagged scale coefficient — d_lagscale

<dl>

<dt>delta</dt>
<dd>a numeric vector. Determines the baseline amount of delta
to be applied to each visit.</dd>


<dt>dlag</dt>
<dd>a numeric vector. Determines the scaling to be applied
to <code>delta</code> based upon with visit the ICE occurred on. Must be the same
length as delta.</dd>


<dt>is_post_ice</dt>
<dd>logical vector. Indicates whether a visit is "post-ICE" or
not.</dd>

</dl>

d_lagscale: Calculate delta from a lagged scale coefficient

Description

Usage

Arguments

Details