Provides functions to apply pooling or backward selection
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
continuous, dichotomous, categorical predictors and interaction terms
between all	these type of 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 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.
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>.

foreign (>= 0.8-72), knitr, rmarkdown, testthat, bookdown,
readr
c(
person("Martijn", "Heymans", email = "mw.heymans@amsterdamumc.nl", role=c("cre", "aut"),
comment = c(ORCID = "0000-0002-3889-0921")),
person("Iris", "Eekhout", email = "i.eekhout@tno.nl", role=c("ctb")))
Pooling, backward and forward selection of 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 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 bootstrapping and cluster
bootstrapping. The package further contains functions to pool the model performance
as ROC/AUC, R-squares, scaled Brier score and calibration	plots for logistic
regression models. Internal validation can be	done 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.
Also a function to externally validate logistic prediction models in multiple imputed
datasets is available.
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 [cre, aut] (<https://orcid.org/0000-0002-3889-0921>),
Iris Eekhout [ctb]