lbpmicox

datasets

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

lbpmicox function

A data frame with 2650 observations on the following 18 variables.<dl>
 <dt><code>Impnr</code></dt>
<dd>a numeric vector</dd> <dt><code>patnr</code></dt>
<dd>a numeric vector</dd> <dt><code>Status</code></dt>
<dd>dichotomous event</dd> <dt><code>Time</code></dt>
<dd>continuous follow up time variable</dd> <dt><code>Duration</code></dt>
<dd>continuous</dd> <dt><code>Previous</code></dt>
<dd>dichotomous</dd> <dt><code>Radiation</code></dt>
<dd>dichotomous</dd> <dt><code>Onset</code></dt>
<dd>dichotomous</dd> <dt><code>Age</code></dt>
<dd>continuous</dd> <dt><code>Tampascale</code></dt>
<dd>continuous</dd> <dt><code>Pain</code></dt>
<dd>continuous</dd> <dt><code>Function</code></dt>
<dd>continuous</dd> <dt><code>Satisfaction</code></dt>
<dd>categorical</dd> <dt><code>JobControl</code></dt>
<dd>continuous</dd> <dt><code>JobDemand</code></dt>
<dd>continuous</dd> <dt><code>Social</code></dt>
<dd>continuous</dd> <dt><code>Expectation</code></dt>
<dd>a numeric vector</dd> <dt><code>Expect_cat</code></dt>
<dd>categorical</dd> 
</dl>

Format

Example dataset for psfmi_coxr function — lbpmicox

A data frame with 2650 observations on the following 18 variables.<dl>
 <dt><code>Impnr</code></dt>
<dd>a numeric vector</dd>

 <dt><code>patnr</code></dt>
<dd>a numeric vector</dd>

 <dt><code>Status</code></dt>
<dd>dichotomous event</dd>

 <dt><code>Time</code></dt>
<dd>continuous follow up time variable</dd>

 <dt><code>Duration</code></dt>
<dd>continuous</dd>

 <dt><code>Previous</code></dt>
<dd>dichotomous</dd>

 <dt><code>Radiation</code></dt>
<dd>dichotomous</dd>

 <dt><code>Onset</code></dt>
<dd>dichotomous</dd>

 <dt><code>Age</code></dt>
<dd>continuous</dd>

 <dt><code>Tampascale</code></dt>
<dd>continuous</dd>

 <dt><code>Pain</code></dt>
<dd>continuous</dd>

 <dt><code>Function</code></dt>
<dd>continuous</dd>

 <dt><code>Satisfaction</code></dt>
<dd>categorical</dd>

 <dt><code>JobControl</code></dt>
<dd>continuous</dd>

 <dt><code>JobDemand</code></dt>
<dd>continuous</dd>

 <dt><code>Social</code></dt>
<dd>continuous</dd>

 <dt><code>Expectation</code></dt>
<dd>a numeric vector</dd>

 <dt><code>Expect_cat</code></dt>
<dd>categorical</dd>

 
</dl>

lbpmicox: Example dataset for psfmi_coxr function

Description

Usage

Arguments

Format

Examples