pool_D4

<code>pool_D4</code> The D4 statistic to combine the likelihood ratio tests (LRT)
 across Multiply Imputed datasets according method D4.

Pooling, backward and forward selection of linear, logistic and Cox regression models in
multiply imputed datasets. Backward and forward selection can be done
from the pooled model using Rubin's Rules (RR), the D1, D2, D3, D4 and
the median p-values method. This is also possible for Mixed models.
The models can contain continuous, dichotomous, categorical and restricted
cubic spline predictors and interaction terms between	all these type of predictors.
The stability of the models	can be evaluated using (cluster) bootstrapping. The package
further contains functions to pool model performance measures as ROC/AUC, Reclassification,
R-squared, scaled Brier score, H&L test and calibration	plots for logistic regression models.
Internal validation can be done across multiply imputed datasets with cross-validation or
bootstrapping. The adjusted intercept after shrinkage of pooled regression coefficients
can be obtained. Backward and forward selection as part of internal validation is possible.
A function to externally validate logistic prediction models in multiple imputed
datasets is available and a function to compare models. For Cox models a strata variable
can be included.
Eekhout (2017) <doi:10.1186/s12874-017-0404-7>.
Wiel (2009) <doi:10.1093/biostatistics/kxp011>.
Marshall (2009) <doi:10.1186/1471-2288-9-57>.

Martijn Heymans

psfmi

Prediction Model Pooling, Selection and Performance Evaluation
Across Multiply Imputed Datasets

Iris Eekhout

pool_D4 function

<dl><dt>data</dt>
<dd>Data frame with stacked multiple imputed datasets.
The original dataset that contains missing values must be excluded from the
dataset. The imputed datasets must be distinguished by an imputation variable,
specified under impvar, and starting by 1.</dd>
<dt>nimp</dt>
<dd>A numerical scalar. Number of imputed datasets. Default is 5.</dd>
<dt>impvar</dt>
<dd>A character vector. Name of the variable that distinguishes the
imputed datasets.</dd>
<dt>fm0</dt>
<dd>the null model.</dd>
<dt>fm1</dt>
<dd>the (nested) model to compare. Must be larger than the null model.</dd>
<dt>robust</dt>
<dd>if TRUE a robust LRT is used (algorithm 1 in Chan and Meng), otherwise 
algorithm 2 is used.</dd>
<dt>model_type</dt>
<dd>if TRUE (default) a logistic regression model is fitted, otherwise
a linear regression model is used</dd></dl>

Arguments

Author

Pools the Likelihood Ratio tests across Multiply Imputed datasets ( method D4) — pool_D4

<dl>

<dt>data</dt>
<dd>Data frame with stacked multiple imputed datasets.
The original dataset that contains missing values must be excluded from the
dataset. The imputed datasets must be distinguished by an imputation variable,
specified under impvar, and starting by 1.</dd>


<dt>nimp</dt>
<dd>A numerical scalar. Number of imputed datasets. Default is 5.</dd>


<dt>impvar</dt>
<dd>A character vector. Name of the variable that distinguishes the
imputed datasets.</dd>


<dt>fm0</dt>
<dd>the null model.</dd>


<dt>fm1</dt>
<dd>the (nested) model to compare. Must be larger than the null model.</dd>


<dt>robust</dt>
<dd>if TRUE a robust LRT is used (algorithm 1 in Chan and Meng), otherwise 
algorithm 2 is used.</dd>


<dt>model_type</dt>
<dd>if TRUE (default) a logistic regression model is fitted, otherwise
a linear regression model is used</dd>

</dl>

pool_D4: Pools the Likelihood Ratio tests across Multiply Imputed datasets ( method D4)

Description

Usage

Value

Arguments

Author

References

Examples