Package: frailtypack 3.6.4

Virginie Rondeau

frailtypack: Shared, Joint (Generalized) Frailty Models; Surrogate Endpoints

The following several classes of frailty models using a penalized likelihood estimation on the hazard function but also a parametric estimation can be fit using this R package: 1) A shared frailty model (with gamma or log-normal frailty distribution) and Cox proportional hazard model. Clustered and recurrent survival times can be studied. 2) Additive frailty models for proportional hazard models with two correlated random effects (intercept random effect with random slope). 3) Nested frailty models for hierarchically clustered data (with 2 levels of clustering) by including two iid gamma random effects. 4) Joint frailty models in the context of the joint modelling for recurrent events with terminal event for clustered data or not. A joint frailty model for two semi-competing risks and clustered data is also proposed. 5) Joint general frailty models in the context of the joint modelling for recurrent events with terminal event data with two independent frailty terms. 6) Joint Nested frailty models in the context of the joint modelling for recurrent events with terminal event, for hierarchically clustered data (with two levels of clustering) by including two iid gamma random effects. 7) Multivariate joint frailty models for two types of recurrent events and a terminal event. 8) Joint models for longitudinal data and a terminal event. 9) Trivariate joint models for longitudinal data, recurrent events and a terminal event. 10) Joint frailty models for the validation of surrogate endpoints in multiple randomized clinical trials with failure-time and/or longitudinal endpoints with the possibility to use a mediation analysis model. 11) Conditional and Marginal two-part joint models for longitudinal semicontinuous data and a terminal event. 12) Joint frailty-copula models for the validation of surrogate endpoints in multiple randomized clinical trials with failure-time endpoints. 13) Generalized shared and joint frailty models for recurrent and terminal events. Proportional hazards (PH), additive hazard (AH), proportional odds (PO) and probit models are available in a fully parametric framework. For PH and AH models, it is possible to consider type-varying coefficients and flexible semiparametric hazard function. Prediction values are available (for a terminal event or for a new recurrent event). Left-truncated (not for Joint model), right-censored data, interval-censored data (only for Cox proportional hazard and shared frailty model) and strata are allowed. In each model, the random effects have the gamma or normal distribution. Now, you can also consider time-varying covariates effects in Cox, shared and joint frailty models (1-5). The package includes concordance measures for Cox proportional hazards models and for shared frailty models. 14) Competing Joint Frailty Model: A single type of recurrent event and two terminal events. Moreover, the package can be used with its shiny application, in a local mode or by following the link below.

Authors:Virginie Rondeau [aut, cre], Juan R. Gonzalez [aut], Yassin Mazroui [aut], Audrey Mauguen [aut], Amadou Diakite [aut], Alexandre Laurent [aut], Myriam Lopez [aut], Agnieszka Krol [aut], Casimir L. Sofeu [aut], Julien Dumerc [aut], Denis Rustand [aut], Jocelyn Chauvet [aut], Quentin Le Coent [aut], Romain Pierlot [aut], Lacey Etzkorn [aut], David Hill [cph], John Burkardt [cph], Alan Genz [cph], Ashwith J. Rego [cph]

frailtypack_3.6.4.tar.gz
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frailtypack.pdf |frailtypack.html
frailtypack/json (API)
NEWS

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

Peer review:

Uses libs:
  • fortran– Runtime library for GNU Fortran applications
  • openmp– GCC OpenMP (GOMP) support library
Datasets:
  • bcos - Breast Cosmesis Data
  • colorectal - Follow-up of metastatic colorectal cancer patients: times of new lesions appearance and death
  • colorectalLongi - Follow-up of metastatic colorectal cancer patients : longitudinal measurements of tumor size
  • dataAdditive - Simulated data as a gathering of clinical trials databases
  • dataMultiv - Simulated data for two types of recurrent events and a terminal event
  • dataNCC - Simulated data for recurrent events and a terminal event with weigths using nested case-control design
  • dataNested - Simulated data with two levels of grouping
  • dataOvarian - Advanced Ovarian Cancer dataset
  • gastadj - Advanced Gastric Cancer dataset
  • longDat - Longitudinal semicontinuous biomarker dataset
  • readmission - Rehospitalization colorectal cancer
  • reduce - Delirium in critically ill ICU patients dataset: the REDUCE clinical trial
  • survDat - Survival dataset

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

fortranopenmp

6.28 score 7 stars 1 packages 89 scripts 1.7k downloads 11 mentions 34 exports 71 dependencies

Last updated 10 days agofrom:da07b6b8cd. Checks:OK: 2. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 25 2024
R-4.5-linux-x86_64OKNov 25 2024

Exports:additivePenalclusterCmeasuresDiffepoceepoceevent2frailtyPenalGenfrailtyPenalhazardjointRecCompetjointSurrCopSimuljointSurroCopPenaljointSurroPenaljointSurroPenalSimuljointSurroTKendalljointSurrSimullongiPenalloocvmultivPenalnum.idplotTreatPredJointSurropredictionrunShinysimulatejointRecCompetslopestesubclusterSurvICsurvivalterminaltimedeptrivPenaltrivPenalNLwts

Dependencies:backportsbase64encbootbroombslibcachemclicolorspacecommonmarkcowplotcpp11crayonDerivdigestdoBydplyrfansifarverfastmapfontawesomefsgenericsggplot2gluegtablehtmltoolshttpuvisobandjquerylibjsonlitelabelinglaterlatticelifecyclemagrittrMASSMatrixmemoisemgcvmicrobenchmarkmimemodelrmunsellnlmepillarpkgconfigpromisespurrrR6rappdirsRColorBrewerRcpprlangrootSolvesassscalesshinysourcetoolsstatmodstringistringrsurvC1survivaltibbletidyrtidyselectutf8vctrsviridisLitewithrxtable

Package summary

Rendered fromPackage_summary.Rmdusingknitr::rmarkdownon Nov 25 2024.

Last update: 2024-06-28
Started: 2015-12-09

Readme and manuals

Help Manual

Help pageTopics
General Frailty models: shared, joint and nested frailty models with prediction; Evaluation of Failure-Time Surrogate Endpointsfrailtypack-package frailtypack _PACKAGE
Fit an Additive Frailty model using a semiparametric penalized likelihood estimation or a parametric estimationadditivePenal
Breast Cosmesis Databcos
Identify clusterscluster
Concordance measures in shared frailty and Cox proportional hazard modelsCbootstrapFP cindexes cindexes.B cindexes.frailty cindexes.W Cmeasures statFP
Follow-up of metastatic colorectal cancer patients: times of new lesions appearance and deathcolorectal
Follow-up of metastatic colorectal cancer patients : longitudinal measurements of tumor sizecolorectalLongi
Simulated data as a gathering of clinical trials databasesdataAdditive
Simulated data for two types of recurrent events and a terminal eventdataMultiv
Simulated data for recurrent events and a terminal event with weigths using nested case-control designdataNCC
Simulated data with two levels of groupingdataNested
Advanced Ovarian Cancer datasetdataOvarian
Difference of Expected Prognostic Observed Cross-Entropy (EPOCE) estimators and its 95% tracking interval between two joint models.Diffepoce
Estimators of the Expected Prognostic Observed Cross-Entropy (EPOCE) for evaluating predictive accuracy of joint models.epoce
Identify event2 indicatorevent2
Fit a Shared, Joint or Nested Frailty modelfactor.names frailtyPenal timedep.names waldtest
Advanced Gastric Cancer datasetgastadj
Fit a Shared or a Joint Frailty Generalized Survival ModelGenfrailtyPenal
Hazard function.hazard
Competing Joint Frailty Model: A single type of recurrent event and two terminal events.jointRecCompet
Generate survival times for two endpoints using the joint frailty-copula model for surrogacyjointSurrCopSimul
Fit the one-step Joint frailty-copula model for evaluating a canditate surrogate endpointjointSurroCopPenal
Fit the one-step Joint surrogate model for evaluating a canditate surrogate endpointjointSurroPenal
Simulation studies based on the one-step Joint surrogate models for the evaluation of a canditate surrogate endpointjointSurroPenalSimul
Kendall's tau estimation using numerical integration methodsjointSurroTKendall
Generate survival times for two endpoints using the joint frailty surrogate modeljointSurrSimul
Longitudinal semicontinuous biomarker dataset (TPJM)longDat
Fit a Joint Model for Longitudinal Data and a Terminal EventlongiPenal
The trials leave-one-out crossvalidation for the one-step Joint surrogate model for evaluating a canditate surrogate endpoint.loocv
Fit a multivariate frailty model for two types of recurrent events and a terminal event.for frailty model multivariate multivPenal transfo.table
Identify individuals in Joint model for clustered datanum.id
Plot Method for an Additive frailty model.lines.additivePenal plot.additivePenal
Plot difference of EPOCE estimators between two joint frailty models.plot.Diffepoce
Plot values of estimators of the Expected Prognostic Observed Cross-Entropy (EPOCE).plot.epoce
Plot Method for a Shared frailty model.lines.frailtyPenal plot.frailtyPenal
Plot method for a joint nested frailty model.lines.jointNestedPenal plot.jointNestedPenal
Plot Method for a Joint frailty model.lines.jointPenal plot.jointPenal
Plot Method for a joint competing risk model with one recurrent event and two terminal events.plot.jointRecCompet
Plot Method for a joint surrogate mediation analysis model.plot.jointSurroMed
Plot Method for the one-step Joint surrogate model for the evaluation of a canditate surrogate endpoint.lines.jointSurroPenal plot.jointSurroPenal
Plot of trials leave-one-out crossvalidation Outputs from the one-step Joint surrogate model for evaluating a canditate surrogate endpoint.plot.jointSurroPenalloocv
Plot Method for a joint model for longitudinal data and a terminal event.lines.longiPenal plot.longiPenal
Plot Method for a multivariate frailty model.plot.multivPenal
Plot Method for a Nested frailty model.lines.nestedPenal plot.nestedPenal
Plot predictions using a Cox or a shared frailty model.plot.predFrailty
Plot predictions using a joint frailty model.plot.predJoint
Plot predictions using a joint model for longitudinal data and a terminal event or a trivariate joint model for longitudinal data, recurrent events and a terminal event.plot.predLongi
Plot Method for a trivariate joint model for longitudinal data, recurrent events and a terminal event.lines.trivPenal plot.trivPenal
Plot Method for a Non-Linear Trivariate Joint Model for Recurrent Events and a Terminal Event with a Biomarker Described with an ODE.lines.trivPenalNL plot.trivPenalNL
Plot of the prediction of the treatment effect on the true endpoint and the STEplotTreatPredJointSurro
S3method predict for the one-step Joint surrogate models for the evaluation of a canditate surrogate endpoint.predict.jointSurroPenal
Prediction probabilities for Cox proportional hazard, Shared, Joint frailty models, Joint models for longitudinal data and a terminal event and Trivariate joint model for longitudinal data, recurrent events and a terminal event (linear and non-linear).prediction
Print a Short Summary of parameter estimates of an additive frailty modelprint.additivePenal
Print a short summary of results of Cmeasure function.print.Cmeasures
Print a Short Summary of parameter estimates of a shared frailty modelprint.frailtyPenal
Print a Short Summary of parameter estimates of a joint nested frailty modelprint.jointNestedPenal
Print a Short Summary of parameter estimates of a joint frailty modelprint.jointPenal
Print a Short Summary of parameter estimates of a joint competing risks midelprint.jointRecCompet
Summary of the random effects parameters, the fixed treatment effects, and the surrogacy evaluation criteria estimated from a joint surrogate modelprint.jointSurroPenal
Print a Summary of parameter estimates of a joint model for longitudinal data and a terminal eventprint.longiPenal
Print a Short Summary of parameter estimates of a multivariate frailty modelprint.multivPenal
Print a Short Summary of parameter estimates of a nested frailty modelprint.nestedPenal
Print a short summary of results of prediction function.print.predFrailty print.prediction print.predJoint print.predLongi
Print a Summary of parameter estimates of a joint model for longitudinal data, recurrent events and a terminal eventprint.trivPenal
Print a Summary of parameter estimates of a non-linear trivariate joint model for longitudinal data, recurrent events and a terminal eventprint.trivPenalNL
Rehospitalization colorectal cancerreadmission
Delirium in critically ill ICU patients dataset: the REDUCE clinical trialreduce
Shiny application for modelisation and prediction of frailty modelsrunShiny
Generating from a joint competing Joint frailty model with a recurrent event and two terminal events.simulatejointRecCompet
Identify variable associated with the random slopeslope
Surrogate threshold effect for the one-step Joint surrogate model for the evaluation of a canditate surrogate endpoint.ste
Identify subclusterssubcluster
summary of parameter estimates of an additive frailty modelprint.summary.additivePenal summary.additivePenal
summary of parameter estimates of a shared frailty modelprint.summary.frailtyPenal summary.frailtyPenal
summary of parameter estimates of a joint nested frailty modelprint.summary.jointNestedPenal summary.jointNestedPenal
summary of parameter estimates of a joint frailty modelprint.summary.jointPenal summary.jointPenal
Summary method for a joint competing risks midelsummary.jointRecCompet
Short summary of the random effects parameters, the fixed treatment effects, and the surrogacy evaluation criteria estimated from a joint surrogate mediation modelprint.summary.jointSurroMed summary.jointSurroMed
Short summary of the surrogacy evaluation criteria estimated from a joint surrogate modelsummary.jointSurroPenal
Short summary of the simulation studies based on a joint surrogate modelprint.summary.jointSurroPenalSimul summary.jointSurroPenalSimul
Short summary of fixed covariates estimates of a joint model for longitudinal data and a terminal event.print.summary.longiPenal summary.longiPenal
summary of parameter estimates of a multivariate frailty model.summary.multivPenal
summary of regression coefficient estimates of a nested frailty modelprint.summary.nestedPenal summary.nestedPenal
Short summary of fixed covariates estimates of a joint model for longitudinal data, recurrent events and a terminal eventprint.summary.trivPenal summary.trivPenal
Short summary of fixed covariates estimates of a non-linear trivariate joint model for longitudinal data, recurrent events and a terminal eventprint.summary.trivPenalNL summary.trivPenalNL
Survival dataset (TPJM)survDat
Create a survival object for interval censoring and possibly left truncated dataSurvIC
Survival functionsurvival
Identify terminal indicatorterminal
Identify time-varying effectstimedep
Fit a Trivariate Joint Model for Longitudinal Data, Recurrent Events and a Terminal EventtrivPenal
Fit a Non-Linear Trivariate Joint Model for Recurrent Events and a Terminal Event with a Biomarker Described with an ODE Population ModeltrivPenalNL
Identify weightswts