psfmi_stab

<code>psfmi_stab</code> Stability analysis of predictors and prediction models selected with
 the <code>psfmi_lr</code>, <code>psfmi_coxr</code> or <code>psfmi_mm</code> functions of the <code>psfmi</code> package.

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

psfmi_stab 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>, <code>psfmi_coxr</code> or <code>psfmi_mm</code>.</dd>
<dt>boot_method</dt>
<dd>A single string to define the bootstrap method. Use "single" after a call to
<code>psfmi_lr</code> and <code>psfmi_coxr</code> and "cluster" after a call to <code>psfmi_mm</code>.</dd>
<dt>nboot</dt>
<dd>A numerical scalar. Number of bootstrap samples to evaluate the stability. Default is 20.</dd>
<dt>p.crit</dt>
<dd>A numerical scalar. Used as P-value selection criterium during bootstrap model selection.</dd>
<dt>start_model</dt>
<dd>If TRUE the bootstrap evaluation takes place from the start model of object pobj, if
FALSE the final model is used for the evaluation.</dd>
<dt>direction</dt>
<dd>The direction of predictor selection, "BW" for backward selection and "FW"
 for forward selection.
#'</dd></dl>

Arguments

https://mwheymans.github.io/psfmi/articles/psfmi_StabilityAnalysis.html

Vignettes

Function to evaluate bootstrap predictor and model stability in multiply imputed datasets. — psfmi_stab

<dl>

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


<dt>boot_method</dt>
<dd>A single string to define the bootstrap method. Use "single" after a call to
<code>psfmi_lr</code> and <code>psfmi_coxr</code> and "cluster" after a call to <code>psfmi_mm</code>.</dd>


<dt>nboot</dt>
<dd>A numerical scalar. Number of bootstrap samples to evaluate the stability. Default is 20.</dd>


<dt>p.crit</dt>
<dd>A numerical scalar. Used as P-value selection criterium during bootstrap model selection.</dd>


<dt>start_model</dt>
<dd>If TRUE the bootstrap evaluation takes place from the start model of object pobj, if
FALSE the final model is used for the evaluation.</dd>


<dt>direction</dt>
<dd>The direction of predictor selection, "BW" for backward selection and "FW"
 for forward selection.
#'</dd>

</dl>

https://mwheymans.github.io/psfmi/articles/psfmi_StabilityAnalysis.html

psfmi_stab: Function to evaluate bootstrap predictor and model stability in multiply imputed datasets.

Description

Usage

Value

Arguments

Vignettes

Details

References

Examples