genTHMM: Generation of survival data from a time-homogeneous Markov...
In genSurv: Generating Multi-State Survival Data

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

View source: R/genTHMM.R

Generation of survival data from a time-homogeneous Markov model.

1	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. (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. 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., 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.

genCMM, genTDCM, genCPHM.

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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)

   PTNUM        time state covariate
1      1  0.00000000     1  27.29563
2      1  0.34603421     2  27.29563
3      1 75.50008204     2  27.29563
4      2  0.00000000     1  11.29024
5      2  6.03978341     3  11.29024
6      3  0.00000000     1  40.94547
7      3  0.24411471     2  40.94547
8      3 27.54375381     2  40.94547
9      4  0.00000000     1  75.97174
10     4  0.02467472     3  75.97174
11     5  0.00000000     1  72.87969
12     5  0.03255812     2  72.87969
13     5 79.17631030     2  72.87969
14     6  0.00000000     1  41.65639
15     6  0.10750566     3  41.65639
16     7  0.00000000     1  49.21315
17     7  0.07773812     3  49.21315
18     8  0.00000000     1  54.89188
19     8  0.02821177     2  54.89188
20     8 20.37991060     2  54.89188