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

jointSurrCopSimul {frailtypack}

R Documentation

Generate survival times for two endpoints using the joint frailty-copula model for surrogacy

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 `\alpha`. The default is `1.5`.
`gamma`	Fixed value for `\gamma`. The default is `2.5`.
`sigma.s`	Fixed value for `\sigma`²_{v_S}. The default is `0.7`.
`sigma.t`	Fixed value for `\sigma`²_{v_T}. The default is `0.7`.
`cor`	Desired level of correlation between v_{S_i} and v_{T_i}. `R`²_trial = cor ². The default is `0.8`.
`betas`	Vector of the fixed effects for `\beta`_S. The size must be equal to `ver` The default is `c(-1.25,0.5)`.
`betat`	Vector of the fixed effects for `\beta`_T. 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 `u`_i and two correlated random effects treatment-by-trial interaction (v_{S_i}, v_{T_i}), 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 \tau) depend on the values of the copula parameter \theta, given that \tau = \theta /(\theta + 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_{S_i} and v_{T_i} are returned.

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

Examples



# 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)

[Package frailtypack version 3.6.2 Index]