surv_data_dc: Generate a sample of time to event dataset with dependent...
In Kenny-Jing-Xu/survivalMPLdc: Penalised Likelihood for Survival Analysis with Dependent Censoring

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

View source: R/surv_data_dc.R

Generate a sample of time to event dataset with, dependent right censoring based on one of the Archimedean copulas the given Kendall's tau, sample size n and covariates matrix Z.

1	surv_data_dc(n, a, Z, lambda, betas, phis, cons7, cons9, tau, copula, distr.ev, distr.ce)

`n`	the sample size, or the number of the subjects in a sample.
`a`	the shape parameter of baseline hazard for the event time T.
`Z`	the covariate matrix with dimension of n by p, where p is the number of covariates.
`lambda`	the scale parameter of baseline hazard for event time T.
`betas`	the regression coefficient vector of proportional hazard model for the event time T with dimenion of p by 1.
`phis`	the regression coefficient vector of proportional hazard model for dependent censoring time C with dimenion of p by 1.
`cons7`	the parameter of baseline hazard for the dependent censoring time C if assuming an exponential distribution.
`cons9`	the upper limit parameter of uniform distribution for the independent censoring time A, i.e. A~U(0, cons9).
`tau`	the Kendall's correlation coefficient between T and C.
`copula`	the Archemedean copula that captures the dependence between T and C, a characteristc value, i.e. 'independent', 'clayton', 'gumbel' or 'frank'.
`distr.ev`	the distribution of the event time, a characteristc value, i.e. 'weibull' or 'log logit'.
`distr.ce`	the distribution of the dependent censoring time, a characteristc value, i.e. 'exponential' or 'weibull'.

surv_data_dc allows to generate a survival dataset under dependent right censoring, at sample size n, based on one of the Archimedean copula, Kendall's tau, and covariates matrix Z with dimension of n by p. For example, at p=2, we have Z=cbind(Z1, Z2), where Z1 is treatment generated by distribution of bernoulli(0.5), i.e. 1 represents treatment group and 0 represents control group; Z2 is the age generated by distribution of U(-10, 10).

The generated dataset includes three varaibles, which are X_i, δ_i and η_i, i.e. X_i=min(T_i, C_i, A_i), δ_i=I(X_i=T_i) and η_i=I(X_i=C_i), for i=1,…,n. 'T' represents the event time, whose hazard function is

h_T(x)=h_{0T}(x)exp(Z^{\top}β)

, where the baseline hazard can take weibull form, i.e. h_{0T}(x) = ax^{a-1} / λ^a, or log logistic form, i.e.

h_{0T}(x) = \frac{ \frac{ 1 }{ a exp( λ ) } ( \frac{ x }{ exp( λ ) } )^{1/a -1 } }{ 1 + ( \frac{ x }{ exp( λ ) } )^{1/a} }

. 'C' represents the dependent censoring time, whose hazard function is h_{C}(x) = h_{0C}(x)exp( Z^{\top}φ) , where the baseline hazard can take exponential form, i.e. h_{0C}(x)=cons7, or weibull form, i.e. h_{0C}(x) = ax^{a-1} / λ^a.'A' represents the administrative or independent censoring time, where A~U(0, cons9).

A sample of time to event dataset under dependent right censoring, which includes observed time X, event indicator δ and dependent censoring indicator η.

Jing Xu, Jun Ma, Thomas Fung

Xu J, Ma J, Connors MH, Brodaty H. (2018). "Proportional hazard model estimation under dependent censoring using copulas and penalized likelihood". Statistics in Medicine 37, 2238–2251.

coxph_mpl_dc

 ##-- Copula types
 copula3 <- 'frank'

 ##-- Marginal distribution for T, C, and A
 a <- 2
 lambda <- 2
 cons7 <- 0.2
 cons9 <- 10
 tau <- 0.8
 betas <- c(-0.5, 0.1)
 phis <- c(0.3, 0.2)
 distr.ev <- 'weibull'
 distr.ce <- 'exponential'

 ##-- Sample size
 n <- 200

 ##-- One sample Monte Carlo dataset
 cova <- cbind(rbinom(n, 1, 0.5), runif(n, min=-10, max=10))
 surv <- surv_data_dc(n, a, cova, lambda, betas, phis, cons7, cons9,
                     tau, copula3, distr.ev, distr.ce)
 n <- nrow(cova)
 p <- ncol(cova)
 ##-- event and dependent censoring proportions
 colSums(surv)[c(2,3)]/n
 X <- surv[,1] # Observed time
 del<-surv[,2] # failure status
 eta<-surv[,3] # dependent censoring status