jointSurrSimul: Generate survival times for two endpoints using the joint...

Description Usage Arguments Details Value Author(s) References See Also Examples

View source: R/jointSurrSimul.R

Description

Date are generated from the one-step joint surrogate model (see jointSurroPenal for more details)

Usage

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jointSurrSimul(
  n.obs = 600,
  n.trial = 30,
  cens.adm = 549.24,
  alpha = 1.5,
  theta = 3.5,
  gamma = 2.5,
  zeta = 1,
  sigma.s = 0.7,
  sigma.t = 0.7,
  cor = 0.8,
  betas = -1.25,
  betat = -1.25,
  frailt.base = 1,
  lambda.S = 1.8,
  nu.S = 0.0045,
  lambda.T = 3,
  nu.T = 0.0025,
  ver = 1,
  typeOf = 1,
  equi.subj.trial = 1,
  equi.subj.trt = 1,
  prop.subj.trial = NULL,
  prop.subj.trt = NULL,
  full.data = 0,
  random.generator = 1,
  random = 0,
  random.nb.sim = 0,
  seed = 0,
  nb.reject.data = 0,
  pfs = 0
)

Arguments

n.obs

Number of considered subjects. The default is 600.

n.trial

Number of considered trials. The default is 30.

cens.adm

censorship time. The default is 549, for about 40% of censored subjects.

alpha

Fixed value for α. The default is 1.5.

theta

Fixed value for θ. The default is 3.5.

gamma

Fixed value for γ. The default is 2.5.

zeta

Fixed value for ζ. The default is 1.

sigma.s

Fixed value for σ2vS. The default is 0.7.

sigma.t

Fixed value for σ2vT. The default is 0.7.

cor

Desired level of correlation between vSi and vTi. R2trial = cor 2. The default is 0.8.

betas

Fixed value for βS. The default is -1.25.

betat

Fixed value for βT. The default is -1.25.

frailt.base

considered the heterogeneity on the baseline risk (1) or not (0). The default is 1.

lambda.S

Desired scale parameter for the Weibull distribution associated with the Surrogate endpoint. The default is 1.8.

nu.S

Desired shape parameter for the Weibull distribution associated with the Surrogate endpoint. The default is 0.0045.

lambda.T

Desired scale parameter for the Weibull distribution associated with the True endpoint. The default is 3.

nu.T

Desired shape parameter for the Weibull distribution associated with the True endpoint. The default is 0.0025.

ver

Number of covariates. For surrogte evaluation, we just considered one covatiate, the treatment arm

typeOf

Type of joint model used for data generation: 0 = classical joint model with a shared individual frailty effect (Rondeau, 2007), 1 = joint surrogate model with shared frailty effects ui and ωij, and two correlated random effects treatment-by-trial interaction (vSi, vTi) as described in Sofeu et al. (2018).

equi.subj.trial

A binary variable that indicates if the same proportion of subjects should be included per trial (1) or not (0). If 0, the proportions of subject per trial are required in parameter prop.subj.trial.

equi.subj.trt

A binary variable that indicates if the same proportion of subjects is randomized per trial (1) or not (0). If 0, the proportions of subject per trial are required in parameter prop.subj.trt.

prop.subj.trial

The proportions of subjects per trial. Requires if equi.subj.trial=0.

prop.subj.trt

The proportions of randomized subject per trial. Requires if equi.subj.trt=0.

full.data

Specified if you want the function to return the full dataset (1), including the random effects, or the restictive dataset (0) with 7 columns required for the function jointSurroPenal.

random.generator

Random number generator used by the Fortran compiler, 1 for the intrinsec subroutine Random_number and 2 for the subroutine uniran(). The default is 1.

random

A binary that says if we reset the random number generation with a different environment at each call (1) or not (0). If it is set to 1, we use the computer clock as seed. In the last case, it is not possible to reproduce the generated datasets. The default is 0. Required if random.generator is set to 1.

random.nb.sim

required if random.generator is set to 1, and if random is set to 1.

seed

The seed to use for data (or samples) generation. Required if the argument random.generator is set to 1. Must be a positive value. If negative, the program do not account for seed. The default is 0.

nb.reject.data

Number of generation to reject before the considered dataset. This parameter is required when data generation is for simulation. With a fixed parameter and random.generator set to 1, all ganerated data are the same. By varying this parameter, different datasets are obtained during data genarations. The default value is 0, in the event of one dataset.

pfs

Is used to specify if the time to progression should be censored by the death time (0) or not (1). The default is 0. In the event with pfs set to 1, death is included in the surrogate endpoint as in the definition of PFS or DFS.

Details

We just considered in this generation, the Gaussian random effects. If the parameter full.data is set to 1, this function return a list containning severals parameters, including the generated random effects. the desired individual level correlation (Kendall's τ) depend on the values of α, θ, γ and ζ.

Value

This function return if the parameter full.data is set to 0, a data.frame with columns :

patientID

A numeric, that represents the patient's identifier, must be unique;

trialID

A numeric, that represents the trial in which each patient was randomized;

trt

The treatment indicator for each patient, with 1 = treated, 0 = untreated;

timeS

The follow up time associated with the surrogate endpoint;

statusS

The event indicator associated with the surrogate endpoint. Normally 0 = no event, 1 = event;

timeT

The follow up time associated with the true endpoint;

statusT

The event indicator associated with the true endpoint. Normally 0 = no event, 1 = event;

If the argument full.data is set to 1, additionnal colums corresponding to random effects ωij, ui, vSi and vTi are returned. Note that ui, vSi and vTi are returned if typeOf is set to 1

Author(s)

Casimir Ledoux Sofeu casimir.sofeu@u-bordeaux.fr, scl.ledoux@gmail.com and Virginie Rondeau virginie.rondeau@inserm.fr

References

Rondeau V., Mathoulin-Pelissier S., Jacqmin-Gadda H., Brouste V. and Soubeyran P. (2007). Joint frailty models for recurring events and death using maximum penalized likelihood estimation: application on cancer events. Biostatistics 8(4), 708-721.

Sofeu, C. L., Emura, T., and Rondeau, V. (2019). One-step validation method for surrogate endpoints using data from multiple randomized cancer clinical trials with failure-time endpoints. Statistics in Medicine 38, 2928-2942.

See Also

jointSurrSimul

Examples

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## Not run: 
data.sim <- jointSurrSimul(n.obs=600, n.trial = 30,cens.adm=549.24, 
            alpha = 1.5, theta = 3.5, gamma = 2.5, sigma.s = 0.7, 
            zeta = 1, sigma.t = 0.7, cor = 0.8, betas = -1.25, 
            betat = -1.25, full.data = 0, random.generator = 1, 
            seed = 0, nb.reject.data = 0, pfs = 0)

## End(Not run)

frailtypack documentation built on Dec. 20, 2021, 5:07 p.m.