Description Usage Arguments Details Value Examples
View source: R/simData4iCoxph.R
Generate survival data with uncertain records. An integrative Cox model can
be fitted for the simulated data by function iCoxph
.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21  simData4iCoxph(
nSubject = 1000,
beta0Vec,
xMat,
maxNum = 2,
nRecordProb = c(0.9, 0.1),
matchCensor = 0.1,
matchEvent = 0.1,
censorMin = 0.5,
censorMax = 12.5,
lambda = 0.005,
rho = 0.7,
fakeLambda1 = lambda * exp(3),
fakeRho1 = rho,
fakeLambda2 = lambda * exp(3),
fakeRho2 = rho,
mixture = 0.5,
randomMiss = TRUE,
eventOnly = FALSE,
...
)

nSubject 
Number of subjects. 
beta0Vec 
Timeinvariant covariate coefficients. 
xMat 
Design matrix. By default, three continuous variables following standard normal distribution and one binary variable following Bernoulli distribution with equal probability are used. 
maxNum 
Maximum number of uncertain records. 
nRecordProb 
Probability of the number of uncertain records. 
matchCensor 
The matching rate for subjects actually having censoring times. 
matchEvent 
The matching rate for subjects actually having event times. 
censorMin 
The lower boundary of the uniform distribution for generating censoring time. 
censorMax 
The upper boundary of the uniform distribution for generating censoring time. 
lambda 
A positive number, scale parameter in baseline rate function for true event times. 
rho 
A positive number, shape parameter in baseline rate function for true event times. 
fakeLambda1 
A positive number, scale parameter in baseline rate function for fake event times from one distribution. 
fakeRho1 
A positive number, shape parameter in baseline rate function for fake event times from one distribution. 
fakeLambda2 
A positive number, scale parameter in baseline rate function for fake event times from another distribution. 
fakeRho2 
A positive number, shape parameter in baseline rate function for fake event times from another distribution. 
mixture 
The mixture weights, i.e., the probabilities (summing up to one) of fake event times coming from different mixture components. 
randomMiss 
A logical value specifying whether the labels of the true
records are missing completely at random (MCAR) or missing not at random
(MNAR). The default value is 
eventOnly 
A logical value specifying whether the uncertain records
only include possible events. The default value is 
... 
Other arguments for future usage. 
The event times are simulated from a Weibull proportional hazard model of given shape and baseline scale. The censoring times follow uniform distribution of specified boundaries.
A data frame with the following columns,
ID
: subject ID
time
: observed event times
event
: event indicators
isTure
: latent labels indicating the true records
and the corresponding covariates.
1  ## See examples of function iCoxph

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