genCMM: Generation of Cox Markov data from an illness-death model
In genSurv: Generating Multi-State Survival Data

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

View source: R/genCMM.R

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

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

`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).

The Weibull distribution with shape parameter λ and scale parameter θ has hazard function given by:

α(t)=λθ t^{θ-1}

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).

Artur Araújo, Luís Meira Machado and Susana Faria

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.

genCPHM, genTDCM, genTHMM.

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