test: Simulated data with clustered competing risks
In frailtyHL: Frailty Models via Hierarchical Likelihood

Description Usage Format References

A data set for the cause-specific hazard frailty model assuming a bivariate normal distribution is generated using a technique similar to Beyersmann et al. (2009) and Christian et al. (2016). Let there be two event types, Types 1 and 2, as well as independent censoring. Consider a sample size n = 100 with (q, ni) = (50, 3). Here, q is the number of clusters and ni is the cluster size. The random effects (log-frailties) are from bivariate normal with mean vector (0,0) and variance-covariance matrix having (1,1,-0.5). Data are generated from the conditional cause-specific hazard rates for each event type given the random effects. Here, for Type 1 event the two true regression parameters are (0.6, -0.4) with a constant baseline hazard 2 and for Type 2 event the true parameters are (-0.3, 0.7) with a constant baseline hazard 0.5, respectively. The covariates x1 and x2 are generated from a standard normal distribution and a Bernoulli distribution with probability 0.5, respectively. Censoring times are generated from a Uniform(0, 1.3) distribution. Under this scenario, with 25.2% censoring, the proportions of Type 1 and Type 2 events are 53.2% and 21.6%, respectively.

1	data("test")

A data frame with 250 observations on the following 6 variables.

obs: Observation number
id: Id number
time: Time to event
status: Event indicator(2=Type 2 event, 1=Type 1 event, 0=censored)
x1: A covariate from standard normal distribution
x2: A covariate from Bernoulli normal distribution

Beyersmann, J., Dettenkofer, M., Bertz, H. and Schumacher, M. (2007). A competing risks analysis of bloodstream infection after stem-cell transplantation using subdistribution hazardsa and cause-specific hazards. Statistics in Medicine, 26, 5360-5369.

Christian, N. J., Ha, I. D. and Jeong, J. H. (2016). Hierarchical likelihood inference on clustered competing risks data. Statistics in Medicine, 35, 251-267.

Ha, I. D., Jeong, J. H. and Lee, Y. (2017). Statistical modelling of survival data with random effects: h-likelihood approach. Springer, in press.

frailtyHL documentation built on Dec. 1, 2019, 1:25 a.m.