estimateCensoring: Estimate Censoring Mechanisms

In survtmle: Compute Targeted Minimum Loss-Based Estimates in Right-Censored Survival Settings

Description

Computes an estimate of the hazard for censoring using either glm or SuperLearner based on log-likelihood loss. The function then computes the censoring survival distribution based on these estimates. The structure of the function is specific to how it is called within survtmle. In particular, dataList must have a very specific structure for this function to run properly. The list should consist of data.frame objects. The first will have the number of rows for each observation equal to the ftime corresponding to that observation. The subsequent entries will have t0 rows for each observation and will set trt column equal to each value of trtOfInterest in turn. One of these columns must be named C that is a counting process for the right-censoring variable. The function will fit a regression with C as the outcome and functions of trt and names(adjustVars) as specified by glm.ctime or SL.ctime as predictors.

Usage

estimateCensoring(dataList, adjustVars, t0, SL.ctime = NULL,
  glm.ctime = NULL, glm.family, returnModels = FALSE, verbose = TRUE,
  gtol = 0.001, ...)

Arguments

dataList	A list of data.frame objects as described in ?makeDataList.
adjustVars	Object of class data.frame that contains the variables to adjust for in the regression.
t0	The timepoint at which survtmle was called to evaluate. Needed only because the naming convention for the regression if t == t0 is different than if t != t0.
SL.ctime	A character vector or list specification to be passed to the SL.library argument in the call to SuperLearner for the outcome regression (either cause-specific hazards or conditional mean). See ?SuperLearner for more information on how to specify valid SuperLearner libraries. It is expected that the wrappers used in the library will play nicely with the input variables, which will be called "trt" and names(adjustVars).
glm.ctime	A character specification of the right-hand side of the equation passed to the formula option of a call to glm for the outcome regression (either cause-specific hazards or conditional mean). Ignored if SL.ctime != NULL. Use "trt" to specify the treatment in this formula (see examples). The formula can additionally include any variables found in names(adjustVars).
glm.family	The type of regression to be performed if fitting GLMs in the estimation and fluctuation procedures. The default is "binomial" for logistic regression. Only change this from the default if there are justifications that are well understood. This is inherited from the calling function (either mean_tmle or hazard_tmle).
returnModels	A boolean indicating whether to return the SuperLearner or glm objects used to estimate the nuisance parameters. Must be set to TRUE if the user plans to use calls to timepoints to obtain estimates at times other than t0. See ?timepoints for more information.
verbose	A boolean indicating whether the function should print messages to indicate progress.
gtol	The truncation level of predicted censoring survival to handle positivity violations.
...	Other arguments. Not currently used.

Value

The function returns a list that is exactly the same as the input dataList, but with a column named G_dC added to it, which is the estimated conditional survival distribution for the censoring variable evaluated at the each of the rows of each data.frame in dataList.

survtmle documentation built on May 2, 2019, 9:44 a.m.