genTHMM {genSurv} | R Documentation |
Generation of survival data from a time-homogeneous Markov model.
genTHMM(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 three. We assume an exponential baseline hazard function with constant hazard rate for each transition. |
An object with two classes, data.frame
and THMM
.
For generating survival data from the THMM model, the counting process data structure must contain the following variables:
id
, time
, state
, covariate
. Each patient is identified by id.
The variable time
represents time for each interval of follow-up while variable state
denotes the state of the individual.
Variable covariate
is the (time-fixed) covariate to be studied in the regression model.
Individuals without change in the time dependent covariate are represented by two lines of data,
whereas patients with a change in the time-dependent covariate must be represented by three lines.
Artur Araújo, Luís Meira Machado and Susana Faria
Jackson, C. (2007). Multi-state modelling with R: the msm package, Cambridge, U.K.
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.
Therneau, T.M., Grambsch, P.M. (2000). Modelling survival data: Extending the Cox Model. New York: Springer.
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) ) head(thmmdata, n=20L)