jointSurrSimul: Generate survival times for two endpoints using the joint...
In socale/frailtypack: Shared, Joint (Generalized) Frailty Models; Surrogate Endpoints

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

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

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
)

`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 σ²_{v_S}. The default is `0.7`.
`sigma.t`	Fixed value for σ²_{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`	Fixed value for β_S. The default is `-1.25`.
`betat`	Fixed value for β_T. 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 `u`_i and ω_ij, and two correlated random effects treatment-by-trial interaction (v_{S_i}, v_{T_i}) 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.

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.

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 ω_ij, u_i, v_{S_i} and v_{T_i} are returned. Note that u_i, v_{S_i} and v_{T_i} are returned if typeOf is set to 1

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

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.

jointSurrSimul

## 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)