psfmi: Prediction Model Selection and Performance Evaluation in Multiple Imputed Datasets

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>.

Version: 0.5.0
Depends: R (≥ 3.6.0)
Imports: survival (> 2.41-3), car (> 3.0.0), lme4 (≥ 1.1-21), norm (≥ 1.0-9.5), miceadds (> 2.10-14), mitools (≥ 2.4), pROC (> 1.11.0), rms (> 5.1-2), ResourceSelection (> 0.3-2), ggplot2 (> 2.2.1), dplyr (≥ 0.8.3), magrittr (≥ 1.5), rsample (≥ 0.0.5), purrr (≥ 0.3.3), tidyr (≥ 1.0.0), tibble (≥ 2.1.3), mice (≥ 3.6.0), mitml (≥ 0.3-7), cvAUC (≥ 1.1.0), stringr (≥ 1.4.0)
Suggests: foreign (≥ 0.8-72), knitr, rmarkdown, testthat, bookdown, readr
Published: 2020-09-24
Author: Martijn Heymans ORCID iD [cre, aut], Iris Eekhout [ctb]
Maintainer: Martijn Heymans <mw.heymans at amsterdamumc.nl>
BugReports: https://github.com/mwheymans/psfmi/issues
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL: https://mwheymans.github.io/psfmi/
NeedsCompilation: no
Materials: README NEWS
In views: MissingData
CRAN checks: psfmi results

Downloads:

Reference manual: psfmi.pdf
Vignettes: MI_boot
MI_cv_naive
boot_MI
cv_MI
cv_MI_RR
psfmi_CoxModels
psfmi_LogisticModels
psfmi_StabilityAnalysis
psfmi_mice
psfmi_valext
Package source: psfmi_0.5.0.tar.gz
Windows binaries: r-devel: psfmi_0.5.0.zip, r-release: psfmi_0.5.0.zip, r-oldrel: psfmi_0.5.0.zip
macOS binaries: r-release: psfmi_0.5.0.tgz, r-oldrel: psfmi_0.5.0.tgz
Old sources: psfmi archive

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