Estimate a survival function under current status sampling

Usage

currstatCIR(
  time,
  event,
  W,
  SL_control = list(SL.library = c("SL.mean"), V = 5, method = "method.NNLS"),
  HAL_control = list(n_bins = c(5, 10), grid_type = c("equal_range", "equal_mass"), V =
    5),
  deriv_method = "m-spline",
  missing_method = "extended",
  eval_region,
  n_eval_pts = 101
)

Arguments

time: n x 1 numeric vector of observed monitoring times
event: n x 1 numeric vector of status indicators of whether an event was observed prior to the monitoring time.
W: Dataframe of covariates
SL_control: List of Super Learner control parameters
HAL_control: List of haldensify control parameters
deriv_method: Method for computing derivative
missing_method: Method for handling nonresponse (extended CIR vs. complete case, just for testing)
eval_region: Region over which to estimate the survival function
n_eval_pts: Number of points in grid on which to evaluate survival function

Value

data frame giving results