Estimate the Censoring Mechanism

est_g_cens(
  C_cens,
  A,
  W,
  samp_weights = rep(1, length(C_cens)),
  fit_type = c("sl", "glm"),
  glm_formula = "C_cens ~ .",
  sl_learners = NULL,
  bound = 0.02
)

Arguments

C_cens: A numeric vector of loss to follow-up indicators.
A: A numeric vector of observed exposure values.
W: A numeric matrix of observed baseline covariate values.
samp_weights: A numeric vector of observation-level sampling weights, as produced by the internal procedure to estimate the two-phase sampling mechanism est_samp.
fit_type: A character indicating whether to use GLMs or Super Learner to fit the censoring mechanism. If option "glm" is selected, the argument glm_formula must NOT be NULL, instead containing a model formula (as per glm) as a character. If the option "sl" is selected, the argument sl_learners must NOT be NULL; instead, an instantiated sl3 Lrnr_sl object, specifying learners and a metalearner for the Super Learner fit, must be provided. Consult the documentation of sl3 for details.
glm_formula: A character giving a formula for fitting a (generalized) linear model via glm.
sl_learners: Object containing a set of instantiated learners from the sl3, to be used in fitting an ensemble model.
bound: numeric giving the lower limit of censoring mechanism estimates to be tolerated (default = 0.02). Estimates below this value are truncated to this or 1/n. See bound_propensity for details.

Value

A numeric vector of the propensity score for censoring.

Details

Compute the censoring mechanism for the observed data, in order to apply a joint intervention for removing censoring by re-weighting.