genCMM {genSurv}

Generation of Cox Markov data from an illness-death model

Description

Generation of Cox Markov data from an illness-death model.

Usage

genCMM(n, model.cens, cens.par, beta, covar, rate)

Arguments

n

Sample size.

model.cens

Model for censorship. Possible values are "uniform" and "exponential".

cens.par

Parameter for the censorship distribution. Must be greater than 0.

beta

Vector of three regression parameters for the three transitions: (beta_12,beta_13,beta_23).

covar

Parameter for generating the time-fixed covariate. An uniform distribution is used.

rate

Vector of dimension six: (shape1, scale1, shape2, scale2, shape3, scale3). A Weibull baseline hazard function is assumed (with two parameters) for each transition (see details below).

Details

The Weibull distribution with shape parameter \lambda and scale parameter \theta has hazard function given by:

\alpha(t)=\lambda\theta t^{\theta-1}

Value

An object with two classes, data.frame and CMM. The data structure used for generating survival data from the Cox Markov Model (CMM) is similar as for the time-dependent Cox model (TDCM). In this case the data structure has one more variable representing the transition (variable trans). trans=1 denotes the transition from State 1 to State 3 (without observing the intermediate event; State 2); trans=2 denotes the transition from State 1 to State 2; and trans=3 denotes the transition from State 2 to State 3 (absorbing).

Author(s)

Artur Araújo, Luís Meira Machado and Susana Faria

References

Meira-Machado, L., Cadarso-Suárez, C., De Uña- Álvarez, J., Andersen, P.K. (2009). Multi-state models for the analysis of time to event data. Statistical Methods in Medical Research, 18(2), 195-222. doi: 10.1177/0962280208092301

Meira-Machado L., Faria S. (2014). A simulation study comparing modeling approaches in an illness-death multi-state model. Communications in Statistics - Simulation and Computation, 43(5), 929-946. doi: 10.1080/03610918.2012.718841

Meira-Machado, L., Roca-Pardiñas, J. (2011). p3state.msm: Analyzing Survival Data from an Illness-Death Model. Journal of Statistical Software, 38(3), 1-18. doi: 10.18637/jss.v038.i03

Meira-Machado, L., Sestelo M. (2019). Estimation in the progressive illness-death model: a nonexhaustive review. Biometrical Journal, 61(2), 245–263. doi: 10.1002/bimj.201700200

Therneau, T.M., Grambsch, P.M. (2000). Modelling survival data: Extending the Cox Model, New York: Springer.

See Also

genCPHM, genTDCM, genTHMM.

Examples

cmmdata <- genCMM( n=1000, model.cens="uniform", cens.par=2.5, beta=c(2,1,-1),
covar=10, rate=c(1,5,1,5,1,5) )
head(cmmdata, n=20L)
library(survival)
fit_13<-coxph(Surv(start,stop,event)~covariate, data=cmmdata, subset=c(trans==1))
fit_13
fit_12<-coxph(Surv(start,stop,event)~covariate, data=cmmdata, subset=c(trans==2))
fit_12
fit_23<-coxph(Surv(start,stop,event)~covariate, data=cmmdata, subset=c(trans==3))
fit_23