as.CMM: Coerce to an object of class 'CMM'
In genSurv: Generating Multi-State Survival Data

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

View source: R/as.CMM.R

Function to coerce objects of class TDCM and THMM to objects of class CMM.

1 2	as.CMM(x) is.CMM(x)

`x`	Any R object.

An object with two classes one being data.frame and the other CMM.

Artur Araújo, Luís Meira Machado and Susana Faria

Cox, D.R. (1972). Regression models and life tables. Journal of the Royal Statistical Society: Series B, 34(2), 187-202. doi: 10.1111/j.2517-6161.1972.tb00899.x

Jackson, C. (2011). Multi-State Models for Panel Data: The msm Package for R. Journal of Statistical Software, 38(8), 1–28. doi: 10.18637/jss.v038.i08

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.

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.

as.TDCM, as.THMM, genCMM, genTDCM, genTHMM.

# generate TDCM data
tdcmdata <- genTDCM(n=100, dist="exponential", corr=0, dist.par=c(1,1),
model.cens="uniform", cens.par=1, beta=c(-3,2), lambda=10)

# coerce TDCM data to CMM data
cmmdata0 <- as.CMM(tdcmdata)
head(cmmdata0, n=20L)

# generate THMM data
thmmdata <- genTHMM( n=100, model.cens="uniform", cens.par=80, beta= c(0.09,0.08,-0.09),
covar=80, rate= c(0.05,0.04,0.05) )

# coerce THMM data to CMM data
cmmdata1 <- as.CMM(thmmdata)
head(cmmdata1, n=20L)