simData4iCoxph function

Simulated Survival Data with Uncertain Records

Generate survival data with uncertain records. An integrative Cox model can be fitted for the simulated data by function iCoxph.

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,
  ...
)

Arguments

• nSubject: Number of subjects.
• beta0Vec: Time-invariant 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 TRUE for MCAR.
• eventOnly: A logical value specifying whether the uncertain records only include possible events. The default value is FALSE, which considers the censoring cases as the possible truth in addition to event records.
• ...: Other arguments for future usage.

Returns

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.

Details

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.

Examples

## See examples of function iCoxph