Weibull.simu {joint.Cox} | R Documentation |
Simulating data from the Weibull joint frailty-copula model
Description
This function generate clustered (grouped) bivariate event times from the joint frailty-copula model with the Weibull baseline hazard functions. Simulating (X_ij,D_ij,C_ij), i=1,2,...,G, and j=1,2,...,N, where G is the number of studies (groups), and N is the number of individuals (patients) within each study. X_ij is time-to-event, D_ij is time-to-death, and C_ij is time-to-censoring. (X_ij, D_ij) and C_ij are independent. Dependence structure on (X_ij, D_ij) is modeled by a copula, which can be the Clayton (default), Frank, Gumbel, or BB1. Covariate effects are specified by the Cox models given a frailty term.
Usage
Weibull.simu(G,N,scale1,scale2,shape1,shape2,beta1,beta2,
eta,copula="Clayton",theta,d=0,alpha,beta12=0,C.max,
cmprsk=FALSE,tau=FALSE,Z.dist=runif,...)
Arguments
G |
The number of studies or groups |
N |
The number of patients within each study |
scale1 |
scale parameter related to the baseline hazard for progression |
scale2 |
scale parameter related to the baseline hazard for death |
shape1 |
shape parameter related to the baseline hazard for progression |
shape2 |
shape parameter related to the baseline hazard for death |
beta1 |
regression coefficients for progression |
beta2 |
regression coefficients for death |
eta |
frailty variance |
copula |
copula function; "Clayton" (default), "Gumbel", "Frank", or "BB1" |
theta |
copula parameter |
d |
BB1 copula's departure parameter from the Clayton (d=0 is the default) |
alpha |
parameter related to frailty, e.g., alpha=1 |
beta12 |
regression coefficients for copula |
C.max |
the upper bound for the censoring distribution |
cmprsk |
if TRUE, simulated data follow the competing risks setting |
tau |
if TRUE, conditional Kendall's tau given Z is shown |
Z.dist |
the distribution of a covariate Z |
... |
parameters for Z.dist |
Details
See Wu et al. (2020) for the algorithms for the Clayton copula. The method was later extended by including covariate effects on a copula (beta12) via the conditional copula model of Emura et al. (2021), The available copulas are the Frank, Gumbel, and BB1 copulas. For the BB1 copula, please see Supplementary Material:Additional simulation studies under the copula misspecification in Emura et al. (2021),
Value
X |
: time to event |
D |
: time to death |
C |
: time to independent censoring |
t.event |
: time to event (=min(X,D,C)) |
event |
: event indicator (=I(X<=D,X<=C)) |
event1 |
: indicator for Event 1 (=I(X<=D,X<=C)) |
t.death |
: time to death (=min(D,C)) |
death |
: death indicator (=I(D<=C)) |
event2 |
: indicator for Event 2 (=I(D<X,D<=C)) |
group |
: study ID (=1,2,...,G) |
Z |
: covariate |
tau |
: Conditional Kendall's tau given Z |
Author(s)
Takeshi Emura
References
Wu BH, Michimae H, Emura T (2020), Meta-analysis of individual patient data with semi-competing risks under the Weibull joint frailty-copula model. Comp Stat 35(4):1525-52
Emura T, Shih JH, Ha ID, Wilke RA (2020), Comparison of the marginal hazard model and the sub-distribution hazard model for competing risks under an assumed copula, Stat Methods Med Res 29(8):2307-27
Emura T, Sofeu C, Rondeau V (2021), Conditional copula models for correlated survival endpoints: individual patient data meta-analysis of randomized controlled trials, Stat Methods Med Res 30(12):2634-50
Supplementary Material:Additional simulation studies under the copula misspecification in "Emura T, Sofeu C, Rondeau V (2021), Conditional copula models for correlated survival endpoints: individual patient data meta-analysis of randomized controlled trials, Stat Methods Med Res 30(12):2634-50"
Examples
Weibull.simu(G=5,N=2,scale1=1,scale2=1,shape1=1,shape2=1,
beta1=1,beta2=1,eta=0.5,theta=2,alpha=1,C.max=5)
Weibull.simu(G=5,N=2,scale1=1,scale2=1,shape1=1,shape2=1,
beta1=1,beta2=1,eta=0.5,copula="Gumbel",theta=2,alpha=1,C.max=5)
Weibull.simu(G=5,N=2,scale1=1,scale2=1,shape1=1,shape2=1,
beta1=1,beta2=1,eta=0.5,theta=2,alpha=1,C.max=5,Z.dist=rbinom,size=1,prob=0.5)
## simulated data follow the competing risks setting
Weibull.simu(G=5,N=2,scale1=1,scale2=1,shape1=1,shape2=1,
beta1=1,beta2=1,eta=0.5,theta=2,alpha=1,C.max=5,cmprsk=TRUE)