Package: survcompare 0.2.0

Diana Shamsutdinova

survcompare: Nested Cross-Validation to Compare Cox-PH, Cox-Lasso, Survival Random Forests

Performs repeated nested cross-validation for Cox Proportionate Hazards, Cox Lasso, Survival Random Forest, and their ensemble. Returns internally validated concordance index, time-dependent area under the curve, Brier score, calibration slope, and statistical testing of non-linear ensemble outperforming the baseline Cox model. In this, it helps researchers to quantify the gain of using a more complex survival model, or justify its redundancy. Equally, it shows the performance value of the non-linear and interaction terms, and may highlight the need of further feature transformation. Further details can be found in Shamsutdinova, Stamate, Roberts, & Stahl (2022) "Combining Cox Model and Tree-Based Algorithms to Boost Performance and Preserve Interpretability for Health Outcomes" <doi:10.1007/978-3-031-08337-2_15>, where the method is described as Ensemble 1.

Authors:Diana Shamsutdinova [aut, cre], Daniel Stahl [aut]

survcompare_0.2.0.tar.gz
survcompare_0.2.0.tar.gz(r-4.5-noble)survcompare_0.2.0.tar.gz(r-4.4-noble)
survcompare_0.2.0.tgz(r-4.4-emscripten)survcompare_0.2.0.tgz(r-4.3-emscripten)
survcompare.pdf |survcompare.html✨
survcompare/json (API)

# Install 'survcompare' in R:
install.packages('survcompare', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

3.00 score 1 stars 10 scripts 219 downloads 20 exports 143 dependencies

Last updated 3 months agofrom:f832b2fd4d. Checks:OK: 2. Indexed: yes.

TargetResultDate
Doc / VignettesOKDec 05 2024
R-4.5-linuxOKDec 05 2024

Exports:ml_hyperparams_srfsimulate_crosstermssimulate_linearsimulate_nonlinearsurv_validatesurvcomparesurvcompare2survcox_cvsurvcox_predictsurvcox_trainsurvcoxlasso_trainsurvsrf_cvsurvsrf_predictsurvsrf_trainsurvsrfens_cvsurvsrfens_predictsurvsrfens_trainsurvsrfstack_cvsurvsrfstack_predictsurvsrfstack_train

Dependencies:backportsbase64encbitbit64bslibcachemcaretcheckmateclassclicliprclockclustercmprskcodetoolscolorspacecpp11crayondata.tabledata.treediagramDiagrammeRdigestdoParalleldplyre1071evaluatefansifarverfastmapfontawesomeforeachforeignFormulafsfuturefuture.applygenericsggplot2glmnetglobalsgluegowergridExtragtablehardhathighrHmischmshtmlTablehtmltoolshtmlwidgetsigraphipredisobanditeratorsjquerylibjsonliteKernSmoothknitrlabelinglatticelavalifecyclelistenvlubridatemagrittrMASSMatrixMatrixModelsmemoisemetsmgcvmimeModelMetricsmultcompmunsellmvtnormnlmennetnumDerivparallellypecpillarpkgconfigplotrixplyrpolsplineprettyunitspROCprodlimprogressprogressrproxyPublishpurrrquantregR6randomForestSRCrangerrappdirsRColorBrewerRcppRcppArmadilloRcppEigenreadrrecipesreshape2riskRegressionrlangrmarkdownrmsrpartrstudioapisandwichsassscalesshapeSparseMSQUAREMstringistringrsurvivalTH.datatibbletidyrtidyselecttimechangetimeDatetimeregtimeROCtinytextzdbutf8vctrsviridisviridisLitevisNetworkvroomwithrxfunyamlzoo

Survcompare_application

Rendered fromsurvcompare_application.Rmdusingknitr::rmarkdownon Dec 05 2024.

Last update: 2024-10-06
Started: 2024-01-23

Readme and manuals

Help Manual

Help pageTopics
Auxiliary function for simulatedata functionslinear_beta
Internal function for getting grid of hyperparameters for random or grid search of size = max_grid_sizeml_hyperparams_srf
Print survcompare objectprint.survcompare
Prints trained survensemble objectprint.survensemble_cv
Simulated sample with survival outcomes with non-linear and cross-term dependenciessimulate_crossterms
Simulated sample with survival outcomes with linear dependenciessimulate_linear
Simulated sample with survival outcomes with non-linear dependenciessimulate_nonlinear
Summary of survcompare resultssummary.survcompare
Prints summary of a trained survensemble_cv objectsummary.survensemble_cv
Calculates time-dependent Brier Scoresurv_brierscore
Computes performance statistics for a survival data given the predicted event probabilitiessurv_validate
Cross-validates and compares Cox Proportionate Hazards and Survival Random Forest models"_PACKAGE" survcompare
Compares two cross-validated models using surv____cv functions of this package.survcompare2
Cross-validates Cox or CoxLasso modelsurvcox_cv
Computes event probabilities from a trained cox modelsurvcox_predict
Trains CoxPH using survival package, or trains CoxLasso (cv.glmnet, lambda.min), and then re-trains survival:coxph on non-zero predictorssurvcox_train
Trains CoxLasso, using cv.glmnet(s="lambda.min")survcoxlasso_train
Calculates survival probability estimated by Kaplan-Meier survival curve Uses polynomial extrapolation in survival function space, using poly(n=3)survival_prob_km
Cross-validates Survival Random Forestsurvsrf_cv
Predicts event probability by a trained Survival Random Forestsurvsrf_predict
Fits randomForestSRC, with tuning by mtry, nodedepth, and nodesize. Underlying model is by Ishwaran et al(2008) https://www.randomforestsrc.org/articles/survival.html Ishwaran H, Kogalur UB, Blackstone EH, Lauer MS. Random survival forests. The Annals of Applied Statistics. 2008;2:841–60.survsrf_train
A repeated 3-fold CV over a hyperparameters gridsurvsrf_tune
Internal function for survsrf_tune(), performs 1 CVsurvsrf_tune_single
Cross-validates predictive performance for SRF Ensemblesurvsrfens_cv
Predicts event probability by a trained sequential ensemble of Survival Random Forest and CoxPHsurvsrfens_predict
Fits an ensemble of Cox-PH and Survival Random Forest (SRF) with internal CV to tune SRF hyperparameters.survsrfens_train
Cross-validates stacked ensemble of the CoxPH and Survival Random Forest modelssurvsrfstack_cv
Predicts event probability by a trained stacked ensemble of Survival Random Forest and CoxPHsurvsrfstack_predict
Trains the stacked ensemble of the CoxPH and Survival Random Forestsurvsrfstack_train