Title: Prediction Model Selection and Performance Evaluation in
person("Martijn", "Heymans", email = "mw.heymans@amsterdamumc.nl", role=c("cre", "aut")),
person("Iris", "Eekhout", email = "i.eekhout@tno.nl", role=c("ctb")))
Provides functions to apply pooling or backward selection
for logistic or Cox regression prediction models in multiple imputed
datasets. Backward selection can be done from the pooled model using
Rubin's Rules (RR), the total covariance matrix (D1 method), pooling
chi-square values (D2 method), pooling likelihood ratio statistics
(D3) or pooling the median p-values. The model can contain
continuous, dichotomous, categorical predictors and interaction terms
between all	type of these predictors. Continuous predictors	can also
be introduced as restricted cubic spline coefficients. It is also possible
to force (spline) predictors or interaction terms in the model during predictor
selection. The package also contains functions to generate apparent model performance
measures over imputed datasets as ROC/AUC, R-squares, fit test values and
calibration	plots. A wrapper function	over Frank Harrell's validate function
is used for that. Bootstrap internal validation is performed in each imputed dataset
and results are pooled. Backward selection as part of internal validation
is optional and recommended. Also a function to externally validate logistic
prediction models in multiple imputed datasets is available.

of logistic, Cox regression and Multilevel (mixed models) prediction
models in multiply imputed datasets. Backward selection can be done
from the pooled model using Rubin's Rules (RR), the D1, D2, D3 and
promising median p-values method. The model can contain
between all	these type of predictors. Continuous predictors	can also
selection. The package includes a function to evaluate the stability
of the models	using bootstrapping and cluster bootstrapping. The package further
contains functions to generate pooled model performance measures in multiply
imputed datasets as ROC/AUC, R-squares, Brier score, fit test values and
calibration	plots for logistic regression models. A function to apply
Bootstrap internal validation	is also available where two methods can be
used to combine bootstrapping and	multiple imputation. One method, boot_MI,
first draws	bootstrap samples and	subsequently performs multiple imputation and with
the other method, MI_boot, first bootstrap samples are drawn from each imputed
dataset before results are combined. The adjusted intercept after shrinkage of
the pooled regression coefficients can be subsequently obtained. Backward selection
as part of internal validation is also an option. Also a function to externally
validate logistic	prediction models in multiple imputed datasets is available.