currstatCIR: Estimate a survival function under current status sampling

In cwolock/survML: Tools for Flexible Survival Analysis Using Machine Learning

Estimate a survival function under current status sampling

Description

Estimate a survival function under current status sampling

Usage

currstatCIR(
  time,
  event,
  X,
  SL_control = list(SL.library = c("SL.mean", "SL.glm"), V = 3),
  HAL_control = list(n_bins = c(5), grid_type = c("equal_mass"), V = 3),
  deriv_method = "m-spline",
  sample_split = FALSE,
  m = 5,
  eval_region,
  n_eval_pts = 101,
  alpha = 0.05,
  sensitivity_analysis = FALSE,
  copula_control = list(taus = c(-0.1, -0.05, 0.05, 0.1))
)

Arguments

time	n x 1 numeric vector of observed monitoring times. For individuals that were never monitored, this can be set to any arbitrary value, including NA, as long as the corresponding event variable is NA.
event	n x 1 numeric vector of status indicators of whether an event was observed prior to the monitoring time. This value must be NA for individuals that were never monitored.
X	n x p dataframe of observed covariate values.
SL_control	List of SuperLearner control parameters. This should be a named list; see SuperLearner documentation for further information.
HAL_control	List of haldensify control parameters. This should be a named list; see haldensify documentation for further information.
deriv_method	Method for computing derivative. Options are "m-spline" (the default, fit a smoothing spline to the estimated function and differentiate the smooth approximation), "linear" (linearly interpolate the estimated function and use the slope of that line), and "line" (use the slope of the line connecting the endpoints of the estimated function).
sample_split	Logical indicating whether to perform inference using sample splitting
m	Number of sample-splitting folds, defaults to 5.
eval_region	Region over which to estimate the survival function.
n_eval_pts	Number of points in grid on which to evaluate survival function. The points will be evenly spaced, on the quantile scale, between the endpoints of eval_region.
alpha	The level at which to compute confidence intervals and hypothesis tests. Defaults to 0.05
sensitivity_analysis	Logical, whether to perform a copula-based sensitivity analysis. Defaults to FALSE
copula_control	A named list of control parameters for the copula-based sensitivity analysis. This should be a named list.

Value

List of data frames giving results. If not performing a sensitivity analysis, a single data frame is returned; if performing a sensitivity analysis, a separate data frame will be returned for each value of the copula association parameter. The results data frames have columns:

t	Time at which survival function is estimated
S_hat_est	Survival function estimate
S_hat_cil	Lower bound of confidence interval
S_hat_ciu	Upper bound of confidence interval

Examples

## Not run: # This is a small simulation example
set.seed(123)
n <- 300
x <- cbind(2*rbinom(n, size = 1, prob = 0.5)-1,
           2*rbinom(n, size = 1, prob = 0.5)-1)
t <- rweibull(n,
              shape = 0.75,
              scale = exp(0.4*x[,1] - 0.2*x[,2]))
y <- rweibull(n,
              shape = 0.75,
              scale = exp(0.4*x[,1] - 0.2*x[,2]))

# round y to nearest quantile of y, just so there aren't so many unique values
quants <- quantile(y, probs = seq(0, 1, by = 0.05), type = 1)
for (i in 1:length(y)){
  y[i] <- quants[which.min(abs(y[i] - quants))]
}
delta <- as.numeric(t <= y)

dat <- data.frame(y = y, delta = delta, x1 = x[,1], x2 = x[,2])

dat$delta[dat$y > 1.8] <- NA
dat$y[dat$y > 1.8] <- NA
eval_region <- c(0.05, 1.5)
res <- survML::currstatCIR(time = dat$y,
                           event = dat$delta,
                           X = dat[,3:4],
                           SL_control = list(SL.library = c("SL.mean", "SL.glm"),
                                             V = 3),
                           HAL_control = list(n_bins = c(5),
                                              grid_type = c("equal_mass"),
                                              V = 3),
                           sensitivity_analysis = FALSE,
                           eval_region = eval_region)$primary_results

xvals = res$t
yvals = res$S_hat_est
fn=stepfun(xvals, c(yvals[1], yvals))
plot.function(fn, from=min(xvals), to=max(xvals))
## End(Not run)

cwolock/survML documentation built on April 17, 2025, 5:17 p.m.