Shared, Joint (Generalized) Frailty Models; Surrogate Endpoints
Plot difference of EPOCE estimators between two joint frailty models.
Print a Short Summary of parameter estimates of a joint nested frailty...
Print a Short Summary of parameter estimates of a joint frailty model
Print a Short Summary of parameter estimates of a joint competing risk...
Summary of the random effects parameters, the fixed treatment effects,...
Hazard function.
Print a Short Summary of parameter estimates of an additive frailty mo...
Print a short summary of results of Cmeasure function.
Identify event2 indicator
General Frailty models: shared, joint and nested frailty models with p...
Fit a Shared, Joint or Nested Frailty model
Fit a Shared or a Joint Frailty Generalized Survival Model
Fit an Additive Frailty model using a semiparametric penalized likelih...
Identify clusters
Concordance measures in shared frailty and Cox proportional hazard mod...
Difference of Expected Prognostic Observed Cross-Entropy (EPOCE) estim...
Estimators of the Expected Prognostic Observed Cross-Entropy (EPOCE) f...
Competing Joint Frailty Model: A single type of recurrent event and tw...
Generate survival times for two endpoints using the joint frailty-copu...
Fit the one-step Joint frailty-copula model for evaluating a canditate...
Fit the one-step Joint surrogate model for evaluating a canditate surr...
Simulation studies based on the one-step Joint surrogate models for th...
Kendall's estimation using numerical integration methods
Generate survival times for two endpoints using the joint frailty surr...
Fit a Joint Model for Longitudinal Data and a Terminal Event
The trials leave-one-out crossvalidation for the one-step Joint surrog...
Fit a multivariate frailty model for two types of recurrent events and...
Identify individuals in Joint model for clustered data
Plot Method for an Additive frailty model.
Plot values of estimators of the Expected Prognostic Observed Cross-En...
Plot Method for a Shared frailty model.
Plot method for a joint nested frailty model.
Plot Method for a Joint frailty model.
Print a Short Summary of parameter estimates of a shared frailty model
Plot Method for a joint competing risk model with one recurrent event ...
Plot Method for a joint surrogate mediation analysis model.
Plot Method for the one-step Joint surrogate model for the evaluation ...
Plot of trials leave-one-out crossvalidation Outputs from the one-step...
Plot Method for a joint model for longitudinal data and a terminal eve...
Plot Method for a multivariate frailty model.
Plot Method for a Nested frailty model.
Plot predictions using a Cox or a shared frailty model.
Plot predictions using a joint frailty model.
Plot predictions using a joint model for longitudinal data and a termi...
Plot Method for a trivariate joint model for longitudinal data, recurr...
Plot Method for a Non-Linear Trivariate Joint Model for Recurrent Even...
Plot of the prediction of the treatment effect on the true endpoint an...
S3method predict for the one-step Joint surrogate models for the evalu...
Prediction probabilities for Cox proportional hazard, Shared, Joint fr...
Print a Summary of parameter estimates of a joint model for longitudin...
Print a Short Summary of parameter estimates of a multivariate frailty...
Print a Short Summary of parameter estimates of a nested frailty model
Print a short summary of results of prediction function.
Print a Summary of parameter estimates of a joint model for longitudin...
Print a Summary of parameter estimates of a non-linear trivariate join...
Shiny application for modelisation and prediction of frailty models
Generating from a joint competing Joint frailty model with a recurrent...
Identify variable associated with the random slope
Surrogate threshold effect for the one-step Joint surrogate model for ...
Identify subclusters
summary of parameter estimates of an additive frailty model
summary of parameter estimates of a shared frailty model
summary of parameter estimates of a joint nested frailty model
summary of parameter estimates of a joint frailty model
Summary method for a joint competing risks midel
Short summary of the random effects parameters, the fixed treatment ef...
Short summary of the surrogacy evaluation criteria estimated from a jo...
Short summary of the simulation studies based on a joint surrogate mod...
Short summary of fixed covariates estimates of a joint model for longi...
summary of parameter estimates of a multivariate frailty model.
summary of regression coefficient estimates of a nested frailty model
Short summary of fixed covariates estimates of a joint model for longi...
Short summary of fixed covariates estimates of a non-linear trivariate...
Create a survival object for interval censoring and possibly left trun...
Survival function
Identify terminal indicator
Identify time-varying effects
Fit a Trivariate Joint Model for Longitudinal Data, Recurrent Events a...
Fit a Non-Linear Trivariate Joint Model for Recurrent Events and a Ter...
Identify weights
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.