Description Usage Arguments Details Value Author(s) References See Also Examples
Generates multivariate survival data
1 2 3 4 |
N |
Number of clusters (ids) |
K |
Number of units per cluster |
beta |
Vector of beta coefficients (first number is baseline hazard coefficient (β_{0}), remaining numbers are slope coefficients for covariates (β_{1})) |
cutoff |
Cutoff values for each covariate |
digits |
Rounding digits |
icensor |
Control for censoring rate: 1 - 50% |
model |
Model for simulating data: must be either |
v |
Scale parameter for |
rho |
Correlation for marginal models. Not used in other models |
a |
Parameter for |
lambda |
Parameter for |
This function generates multivariate survival data. Letting i=1,...,N number of clusters, j=1,...,K number of units per cluster, and X_{ij} be a candidate covariate, the following multivariate survival models can be used:
gamma.frailty: \hspace{2mm} λ_{ij}(t)=\exp(β_{0}+β_{1} \cdot I(X_{ij} ≤q c)) w_{i} with w_{i} \sim Γ(1/v, 1/v)
log.normal.frailty: \hspace{2mm} λ_{ij}(t)=\exp(β_{0}+β_{1} \cdot I(X_{ij} ≤q c) + w_{i}) with w_{i} \sim N(0, v)
marginal.multivariate.exponential: \hspace{2mm} λ_{ij}(t)=\exp(β_{0}+β_{1} \cdot I(X_{ij} ≤q c)) absolutely continuous
marginal.nonabsolutely.continuous: \hspace{2mm} λ_{ij}(t)=\exp(β_{0}+β_{1} \cdot I(X_{ij} ≤q c)) not absolutely continuous
nonPH.weibull: \hspace{2mm} λ_{ij}(t)=λ_{0}(t) \exp(β_{0}+β_{1} \cdot I(X_{ij} ≤q c)) w_{i} with w_{i} \sim Γ(1/v ,1/v) and
\hspace{96mm} λ_{0}(t)=α λ t^{α-1}
The user specifies the coefficients (β_{0} and β_{1}), the cutoff values, the censoring rate, and the model with the respective parameters.
dat |
The simulated data |
model |
The model used |
Xiaogang Su, Peter Calhoun, Juanjuan Fan
Fan J., Nunn M., Su X. (2009) Multivariate exponential survival trees and their application to tooth prognosis. Computational Statistics and Data Analysis, 53(4), 1110–1121.
Su X., Fan J., Wang A., Johnson M. (2006) On Simulating Multivariate Failure Times. International Journal of Applied Mathematics & Statistics, 5, 8–18
genSurv, complex.surv.dat.sim, survsim
1 2 3 4 5 | randMarginalExp <- rmultime(N = 200, K = 4, beta = c(-1, 2, 2, 0, 0), cutoff = c(0.5, 0.5, 0, 0),
digits = 1, icensor = 1, model = "marginal.multivariate.exponential", rho = .65)$dat
randFrailtyGamma <- rmultime(N = 200, K = 4, beta = c(-1, 1, 3, 0), cutoff = c(0.4, 0.6, 0),
digits = 1, icensor = 1, model = "gamma.frailty", v = 1)$dat
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