genSurv (version 1.0.4)

as.CMM: Coerce to an object of class CMM

Description

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

Usage

as.CMM(x)
is.CMM(x)

Arguments

x

Any R object.

Value

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

References

Cox, D.R. (1972). Regression models and life tables. Journal of the Royal Statistical Society: Series B, 34(2), 187-202. 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<U+2013>28. 10.18637/jss.v038.i08

Meira-Machado, L., Cadarso-Su<U+00E1>rez, C., De U<U+00F1>a- <U+00C1>lvarez, 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. 10.1080/03610918.2012.718841

Meira-Machado, L., Roca-Pardi<U+00F1>as, J. (2011). p3state.msm: Analyzing Survival Data from an Illness-Death Model. Journal of Statistical Software, 38(3), 1-18. 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<U+2013>263. 10.1002/bimj.201700200

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

See Also

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

Examples

# NOT RUN {
# 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)
# }