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

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

Description

Date are generated from the one-step joint frailty-copula model, under the Claton copula function (see jointSurroCopPenal for more details)

Usage

jointSurrCopSimul(
  n.obs = 600,
  n.trial = 30,
  prop.cens = 0,
  cens.adm = 549,
  alpha = 1.5,
  gamma = 2.5,
  sigma.s = 0.7,
  sigma.t = 0.7,
  cor = 0.9,
  betas = c(-1.25, 0.5),
  betat = c(-1.25, 0.5),
  frailt.base = 1,
  lambda.S = 1.3,
  nu.S = 0.0025,
  lambda.T = 1.1,
  nu.T = 0.0025,
  ver = 2,
  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,
  thetacopule = 6,
  filter.surr = c(1, 1),
  filter.true = c(1, 1),
  covar.names = "trt",
  pfs = 0
)

Arguments

n.obs	Number of considered subjects. The default is 600.
n.trial	Number of considered trials. The default is 30.
prop.cens	A value between 0 and 1, 1-prop.cens is the minimum proportion of people who are randomly censored. Represents the quantile to use for generating the random censorship time. In this case, the censorship time follows a uniform distribution in 1 and (prop.cens)ieme percentile of the generated death times. If this argument is set to 0, the fix censorship is considered. The default is 0.
cens.adm	Censorship time. If argument prop.cens is set to 0, it represents the administrative censorship time, else it represents the fix censoring time. The default is 549, for about 40% of fix censored subjects.
alpha	Fixed value for α. The default is 1.5.
gamma	Fixed value for γ. The default is 2.5.
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	Vector of the fixed effects for βS. The size must be equal to ver The default is c(-1.25,0.5).
betat	Vector of the fixed effects for βT. The size must be equal to ver The default is c(-1.25,0.5).
frailt.base	Considered 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. The mandatory covariate is the treatment arm. The default is 2.
typeOf	Type of joint model used for data generation: 0 = classical joint model with a shared individual frailty effect (Rondeau, 2007), 1 = joint frailty-copula model with shared frailty effects ui and two correlated random effects treatment-by-trial interaction (vSi, vTi), see jointSurroCopPenal.
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 with 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 with 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 at least 7 columns as required for the function jointSurroCopPenal.
random.generator	The 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 generations. The default value is 0, in the event of one dataset.
thetacopule	The desired value for the copula parameter. The default is 6.
filter.surr	Vector of size the number of covariates, with the i-th element that indicates if the hazard for surrogate is adjusted on the i-th covariate (code 1) or not (code 0). By default, 2 covariates are considered.
filter.true	Vector defines as filter.surr, for the true endpoint. filter.true and filter.surr should have the same size
covar.names	Vector of the names of covariables. By default it contains "trt" for the tratment arm. Should contains the names of all covarites wished in the generated 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 the copula parameter θ, given that τ = θ /(θ + 2) under the clayton copula model.

Value

This function returns 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;

and other covariates named Var2, var3, ..., var[ver-1] if ver > 1. If the argument full.data is set to 1, additionnal colums corresponding to random effects u_i, v_Si and v_Ti are returned.

Author(s)

Casimir Ledoux Sofeu [e-mail], [e-mail] and Virginie Rondeau [e-mail]

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. (2020). A joint frailty-copula model for meta-analytic validation of failure time surrogate endpoints in clinical trials. Under review

See Also

jointSurrSimul, jointSurroCopPenal

Examples

## Not run: 
# dataset with 2 covariates and fixed censorship
data.sim <- jointSurrCopSimul(n.obs=600, n.trial = 30, prop.cens = 0, cens.adm=549, 
            alpha = 1.5, gamma = 2.5, sigma.s = 0.7, sigma.t = 0.7, 
            cor = 0.8, betas = c(-1.25, 0.5), betat = c(-1.25, 0.5), 
            full.data = 0, random.generator = 1,ver = 2, covar.names = "trt", 
            nb.reject.data = 0, thetacopule = 6, filter.surr = c(1,1), 
            filter.true = c(1,1), seed = 0)
            
#dataset with 2 covariates and random censorship

data.sim2 <- jointSurrCopSimul(n.obs=600, n.trial = 30, prop.cens = 0.75, 
            cens.adm = 549, alpha = 1.5, gamma = 2.5, sigma.s = 0.7, 
            sigma.t = 0.7, cor = 0.8, betas = c(-1.25, 0.5), 
            betat = c(-1.25, 0.5), full.data = 0, random.generator = 1,
            ver = 2, covar.names = "trt", nb.reject.data = 0, thetacopule = 6, 
            filter.surr = c(1,1), filter.true = c(1,1), seed = 0)
            

## End(Not run)

socale/frailtypack documentation built on June 15, 2021, 3:37 a.m.