This function computes an estimate of the G-computation regression at a
specified time t using either glm or SuperLearner. The
structure of the function is specific to how it is called within
mean_tmle. In particular, wideDataList must have a very
specific structure for this function to run properly. The list should consist
of data.frame objects. The first should have all rows set to their
observed value of trt. The remaining should in turn have all rows set
to each value of trtOfInterest in the survtmle call. Currently
the code requires each data.frame to have named columns for each name
in names(adjustVars), as well as a column named trt. It must
also have a columns named Nj.Y where j corresponds with the numeric
values input in allJ. These are the indicators of failure due to the
various causes before time t and are necessary for determining who to
include in the regression. Similarly, each data.frame should have a
column call C.Y where Y is again t - 1, so that right censored
observations are not included in the regressions. The function will fit a
regression with Qj.star.t+1 (also needed as a column in
wideDataList) on functions of trt and names(adjustVars)
as specified by glm.ftime or SL.ftime.
estimateIteratedMean(wideDataList, t, whichJ, allJ, t0, adjustVars,
SL.ftime = NULL, glm.ftime = NULL, verbose, returnModels = FALSE,
bounds = NULL, ...)A list of data.frame objects.
The timepoint at which to compute the iterated mean.
Numeric value indicating the cause of failure for which regression should be computed.
Numeric vector indicating the labels of all causes of failure.
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.
Object of class data.frame that contains the
variables to adjust for in the regression.
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).
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.ftime != NULL. Use "trt"
to specify the treatment in this formula (see examples). The formula
can additionally include any variables found in
names(adjustVars).
A boolean indicating whether the function should print messages to indicate progress.
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.
A list of bounds to be used when performing the outcome regression (Q) with the Super Learner algorithm. NOT YET IMPLEMENTED.
Other arguments. Not currently used.
The function then returns a list that is exactly the same as the
input wideDataList, but with a column named Qj.t added
to it, which is the estimated conditional mean of Qj.star.t+1
evaluated at the each of the rows of each data.frame in
wideDataList.