Package: pencal 2.2.2

pencal: Penalized Regression Calibration (PRC) for the Dynamic Prediction of Survival

Computes penalized regression calibration (PRC), a statistical method for the dynamic prediction of survival when many longitudinal predictors are available. PRC is described in Signorelli (2024) <doi:10.48550/arXiv.2309.15600> and in Signorelli et al. (2021) <doi:10.1002/sim.9178>.

Authors:Mirko Signorelli [aut, cre, cph], Pietro Spitali [ctb], Roula Tsonaka [ctb], Barbara Vreede [ctb]

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pencal.pdf |pencal.html✨
pencal/json (API)
NEWS

# Install 'pencal' in R:

install.packages('pencal', repos = c('https://m-signo.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/mirkosignorelli/r/issues

Datasets:

fitted_prclmm - A fitted PRC LMM
fitted_prcmlpmm - A fitted PRC MLPMM
pbc2data - Pbc2 dataset

On CRAN:

15 exports 0.49 score 121 dependencies 10 scripts 1.1k downloads

Last updated 3 months agofrom:cf112b75b0. Checks:OK: 5 NOTE: 2. Indexed: yes.

Target	Result	Date
Doc / Vignettes	OK	Sep 11 2024
R-4.5-win	NOTE	Sep 11 2024
R-4.5-linux	NOTE	Sep 11 2024
R-4.4-win	OK	Sep 11 2024
R-4.4-mac	OK	Sep 11 2024
R-4.3-win	OK	Sep 11 2024
R-4.3-mac	OK	Sep 11 2024

Exports:fit_lmms fit_mlpmms fit_prclmm fit_prcmlpmm pencox performance_pencox performance_prc simulate_prclmm_data simulate_prcmlpmm_data simulate_t_weibull summarize_lmms summarize_mlpmms survplot_prc survpred_prclmm survpred_prcmlpmm

Dependencies:abind backports base64enc bootstrap bslib cachem checkmate class cli cluster cmprsk codetools colorspace data.table diagram digest doParallel dplyr evaluate fansi farver fastmap fontawesome foreach foreign Formula fs future future.apply generics ggplot2 glmnet globals glue gridExtra gtable highr Hmisc htmlTable htmltools htmlwidgets ipred isoband iterators jquerylib jsonlite KernSmooth knitr labeling lattice lava lcmm lifecycle listenv magic magrittr marqLevAlg MASS Matrix MatrixModels memoise mets mgcv mime multcomp munsell mvtnorm nlme nnet numDeriv parallelly pillar pkgconfig plotrix polspline prodlim progressr Publish purrr quantreg R6 randtoolbox ranger rappdirs RColorBrewer Rcpp RcppArmadillo RcppEigen riskRegression rlang rmarkdown rmeta rms rngWELL rpart rstudioapi sandwich sass scales shape SparseM SQUAREM stringi stringr SuppDists survcomp survival survivalROC TH.data tibble tidyselect timereg tinytex utf8 vctrs viridis viridisLite withr xfun yaml zoo

pencal: an R Package for the Dynamic Prediction of Survival with Many Longitudinal Predictors

Rendered fromvignette.Rnwusingutils::Sweaveon Sep 11 2024.

Last update: 2024-06-13
Started: 2023-09-16

Citation

Readme and manuals

Help Manual

Help page	Topics
Step 1 of PRC-LMM (estimation of the linear mixed models)	fit_lmms
Step 1 of PRC-MLPMM (estimation of the linear mixed models)	fit_mlpmms
Step 3 of PRC-LMM (estimation of the penalized Cox model(s))	fit_prclmm
Step 3 of PRC-MLPMM (estimation of the penalized Cox model(s))	fit_prcmlpmm
A fitted PRC LMM	fitted_prclmm
A fitted PRC MLPMM	fitted_prcmlpmm
pbc2 dataset	pbc2data
Estimation of a penalized Cox model with time-independent covariates	pencox
Predictive performance of the penalized Cox model with time-independent covariates	performance_pencox
Predictive performance of the PRC-LMM and PRC-MLPMM models	performance_prc
Print method for PRC-LMM model fits	print.prclmm
Print method for PRC-MLPMM model fits	print.prcmlpmm
Simulate data that can be used to fit the PRC-LMM model	simulate_prclmm_data
Simulate data that can be used to fit the PRC-LMM model	simulate_prcmlpmm_data
Generate survival data from a Weibull model	simulate_t_weibull
Step 2 of PRC-LMM (computation of the predicted random effects)	summarize_lmms
Step 2 of PRC-MLPMM (computation of the predicted random effects)	summarize_mlpmms
Extract model fits from step 1 of PRC-LMM	summary.lmmfit
Extract model fits from step 1 of PRC-LMM	summary.mlpmmfit
Summary method for PRC-LMM model fits	summary.prclmm
Summary method for PRC-MLPMM model fits	summary.prcmlpmm
Summary for step 2 of PRC	summary.ranefs
Visualize survival predictions for a fitted PRC model	survplot_prc
Compute the predicted survival probabilities obtained from the PRC models	survpred_prclmm
Compute the predicted survival probabilities obtained from the PRC models	survpred_prcmlpmm