rubin_df

Compute degrees of freedom according to the Barnard-Rubin formula.

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>.

Craig Gower-Page

rbmi

Reference Based Multiple Imputation

Isaac Gravestock

Alessandro Noci

Marcel Wolbers

Daniel Sabanes Bove

F. Hoffmann-La Roche AG 

rubin_df function

<dl><dt>v_com</dt>
<dd>Positive number representing the degrees of freedom in the complete-data analysis.</dd>
<dt>var_b</dt>
<dd>Between-variance of point estimate across multiply imputed datasets.</dd>
<dt>var_t</dt>
<dd>Total-variance of point estimate according to Rubin's rules.</dd>
<dt>M</dt>
<dd>Number of imputations.</dd></dl>

Arguments

Barnard and Rubin degrees of freedom adjustment — rubin_df

<dl>

<dt>v_com</dt>
<dd>Positive number representing the degrees of freedom in the complete-data analysis.</dd>


<dt>var_b</dt>
<dd>Between-variance of point estimate across multiply imputed datasets.</dd>


<dt>var_t</dt>
<dd>Total-variance of point estimate according to Rubin's rules.</dd>


<dt>M</dt>
<dd>Number of imputations.</dd>

</dl>

rubin_df: Barnard and Rubin degrees of freedom adjustment

Description

Usage

Value

Arguments

Details

References