dataMultiv

This contains a simulated sample of of 800 subjects and 1652 observations.
This dataset can be used to illustrate how to fit a joint multivariate
frailty model. Two gaussian correlated random effects were generated with
mean 0, variances 0.5 and a correlation coefficient equals to 0.5. The
coefficients \(\alpha_1\) and \(\alpha_2\) were fixed to 1. The three
baseline hazard functions followed a Weibull distribution and right
censoring was fixed at 5.

datasets

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.  15) functions
to compute power and sample size for four Gamma-frailty-based designs:
Shared Frailty Models, Nested Frailty Models, Joint Frailty Models, and
General Joint Frailty Models. Each design includes two primary functions: a
power function, which computes power given a specified sample size;
and a sample size function, which computes the required sample size to achieve
a specified power. Moreover, the package can be used with its shiny
application, in a local mode or by following the link below.

Virginie Rondeau

frailtypack

Shared, Joint (Generalized) Frailty Models; Surrogate Endpoints

Juan R. Gonzalez

Yassin Mazroui

Audrey Mauguen

Amadou Diakite

Alexandre Laurent

Myriam Lopez

Agnieszka Krol

Casimir L. Sofeu

Julien Dumerc

Denis Rustand

Jocelyn Chauvet

Quentin Le Coent

Romain Pierlot

Lacey Etzkorn

Derek Dinart

Adrien Orué

David Hill

John Burkardt

Alan Genz

Ashwith J. Rego

dataMultiv function

This data frame contains the following columns:<dl>
<dt>PATIENT</dt>
<dd>identification of patient</dd>
 <dt>obs</dt>
<dd>number of observation
for a patient</dd>
 <dt>TIME0</dt>
<dd>start of interval</dd>
 <dt>TIME1</dt>
<dd>end of interval
(death or censoring time)</dd>
 <dt>INDICREC</dt>
<dd>recurrent of type 1 status (0:no,
1:yes)</dd>
 <dt>INDICMETA</dt>
<dd>recurrent of type 2 status (0:no, 1:yes)</dd><dt>INDICDEATH</dt>
<dd>censoring status (0:alive, 1:death)</dd>
 <dt>v1</dt>
<dd>dichotomous
covariate (0,1)</dd>
 <dt>v2</dt>
<dd>dichotomous covariate (0,1)</dd><dt>v3</dt>
<dd>dichotomous covariate (0,1)</dd>
 <dt>TIMEGAP</dt>
<dd>time to event</dd>
 
</dl>

Format

Simulated data for two types of recurrent events and a terminal event — dataMultiv

This data frame contains the following columns:<dl>
<dt>PATIENT</dt>
<dd>identification of patient</dd>
 <dt>obs</dt>
<dd>number of observation
for a patient</dd>
 <dt>TIME0</dt>
<dd>start of interval</dd>
 <dt>TIME1</dt>
<dd>end of interval
(death or censoring time)</dd>
 <dt>INDICREC</dt>
<dd>recurrent of type 1 status (0:no,
1:yes)</dd>
 <dt>INDICMETA</dt>
<dd>recurrent of type 2 status (0:no, 1:yes)</dd>

<dt>INDICDEATH</dt>
<dd>censoring status (0:alive, 1:death)</dd>
 <dt>v1</dt>
<dd>dichotomous
covariate (0,1)</dd>
 <dt>v2</dt>
<dd>dichotomous covariate (0,1)</dd>

<dt>v3</dt>
<dd>dichotomous covariate (0,1)</dd>
 <dt>TIMEGAP</dt>
<dd>time to event</dd>
 
</dl>

Simulated data for two types of recurrent events and a terminal event

dataMultiv: Simulated data for two types of recurrent events and a terminal event

Description

Usage

Arguments

Format