gastadj

This meta-analysis was carried out by the GASTRIC (Global Advanced/Adjuvant Stomach 
Tumor Research international Collaboration) group, using individual data on patients 
with curatively resected gastric cancer. Data from all published randomized trials, 
with a patient recruitment end date before 2004, and comparing adjuvant chemotherapy 
with surgery alone for resectable gastric cancers, were searched electronically. 
The candidate surrogate endpoint S was Desease-free survival time, defined 
as the time (in days) to relapse, second cancer or dead from any cause. The true 
endpoint T was the overall survival time, defined as the time (in days) from 
randomization to death of any cause or to the last follow-up.

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

gastadj function

This data frame contains the following columns:<dl>
<dt>trialID</dt>
<dd>The trial in which the patient was treated</dd><dt>patientID</dt>
<dd>The identification number of a patient</dd>
 
<dt>trt</dt>
<dd>The treatment indicator, coded as 0 = Control and 1 = Experimental</dd><dt>timeS</dt>
<dd>The candidate surrogate (progression-free survival in days)</dd><dt>statusS</dt>
<dd>Censoring indicator for for Progression-free survival 
(0 = alive and progression-free, 1 = with progression or dead)</dd><dt>timeT</dt>
<dd>The true endpoint (overall survival time in days)</dd><dt>statusT</dt>
<dd>Censoring indicator for survival time (0 = alive, 1 = dead)</dd>
</dl>

Format

Advanced Gastric Cancer dataset — gastadj

This data frame contains the following columns:<dl>
<dt>trialID</dt>
<dd>The trial in which the patient was treated</dd>

<dt>patientID</dt>
<dd>The identification number of a patient</dd>
 
<dt>trt</dt>
<dd>The treatment indicator, coded as 0 = Control and 1 = Experimental</dd>

<dt>timeS</dt>
<dd>The candidate surrogate (progression-free survival in days)</dd>

<dt>statusS</dt>
<dd>Censoring indicator for for Progression-free survival 
(0 = alive and progression-free, 1 = with progression or dead)</dd>

<dt>timeT</dt>
<dd>The true endpoint (overall survival time in days)</dd>

<dt>statusT</dt>
<dd>Censoring indicator for survival time (0 = alive, 1 = dead)</dd>


</dl>

Advanced Gastric Cancer dataset

gastadj: Advanced Gastric Cancer dataset

Description

Usage

Arguments

Format