Generates simulated survival data from a trivariate Clayton-Oakes model, which can be used to create example data for trivariate survival function estimation. The marginal distributions are exponential with rate parameter 1. The joint distribution is defined using a Clayton copula. The censoring times are also exponentially distributed with given rate parameters.
genClayton3(n, theta, lambdaC1, lambdaC2, lambdaC3)Sample size for the simulated data set.
Parameter for the Clayton copula. Must be -1 or larger.
Rate parameters for the censoring times. No censoring occurs if this paramter is equal to 0.
A data frame containing the following elements:
Survival times for the simulated data
Censoring indicators for the simulated data
This function simulates data with the following survival function: F(t1,t2,t3) = [F(t1,0,0)^(-theta) + F(0,t2,0)^(-theta) + F(0,0,t3) - 2]^(-1/theta) (The survival function is defined to be equal to 0 if this quantity is negative.) The marginal survival functions F(t1,0,0), F(0,t2,0), and F(0,0,t3) are exponentially distributed with rate parameter 1. After generating survival times Y1, Y2, and Y3 (of length n) under this distribution, censoring times C1, C2, and C3 (also of length n) are generated. C1/C2/C3 are generated under an exponential distribution with rate parameters lambdaC1, lambdaC2, lambdaC3, respectively. If C1[i]<Y1[i] for a given observation i, then observation i is considered to be censored (i.e., Delta1[i]=0). Delta2 and Delta3 are defined in a similar manner. If lambdaC1, lambdaC2, and/or lambdaC3 is equal to 0, then the corresponding variable is uncensored (meaning that Delta[i]=1 for all i).
Clayton, D. "Model for association in bivariate life tables and its application in epidemiological studies of familial tendency in chronic disease incidence.", Biometrika (1978) 65:141-151. Prentice, R., Zhao, S. "The statistical analysis of multivariate failure time data: A marginal modeling approach", CRC Press (2019).
# NOT RUN {
x <- genClayton3(200, 0, 0.5, 0.5, 0.5)
# }
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