pool_compare_models

<code>pool_compare_model</code> Compares the fit and performance of prediction models 
 in multiply imputed data sets by using clinical important performance measures

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_compare_models function

<dl><dt>pobj</dt>
<dd>An object of class <code>pmods</code> (pooled models), produced by a previous
call to <code>psfmi_lr</code>.</dd>
<dt>compare.predictors</dt>
<dd>Character vector with the names of the predictors that are 
compared. See details.</dd>
<dt>compare.group</dt>
<dd>Character vector with the names of the group of predictors that are 
compared. See details.</dd>
<dt>cutoff</dt>
<dd>A numerical scalar. Cutoff used for the categorical NRI value. More than one
cutoff value can be used.</dd>
<dt>boot_auc</dt>
<dd>If TRUE the standard error of the AUC is calculated with stratified
bootstrapping. If FALSE (is default), the standard error is calculated with De Long's
method.</dd>
<dt>nboot</dt>
<dd>A numerical scalar. The number of bootstrap samples for the AUC standard error, used 
when boot_auc is TRUE. Default is 1000.</dd></dl>

Arguments

Compare the fit and performance of prediction models across Multipy Imputed data — pool_compare_models

<dl>

<dt>pobj</dt>
<dd>An object of class <code>pmods</code> (pooled models), produced by a previous
call to <code>psfmi_lr</code>.</dd>


<dt>compare.predictors</dt>
<dd>Character vector with the names of the predictors that are 
compared. See details.</dd>


<dt>compare.group</dt>
<dd>Character vector with the names of the group of predictors that are 
compared. See details.</dd>


<dt>cutoff</dt>
<dd>A numerical scalar. Cutoff used for the categorical NRI value. More than one
cutoff value can be used.</dd>


<dt>boot_auc</dt>
<dd>If TRUE the standard error of the AUC is calculated with stratified
bootstrapping. If FALSE (is default), the standard error is calculated with De Long's
method.</dd>


<dt>nboot</dt>
<dd>A numerical scalar. The number of bootstrap samples for the AUC standard error, used 
when boot_auc is TRUE. Default is 1000.</dd>

</dl>

pool_compare_models: Compare the fit and performance of prediction models across Multipy Imputed data

Description

Usage

Value

Arguments

Details

References

Examples