This function performs a fluctuation of an initial estimate of the
G-computation regression at a specified time t using a call to
glm (i.e., a logistic submodel) or a call to optim (if bounds
are specified). 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. The latter
will be used to obtain predictions that are then mapped into the estimates of
the cumulative incidence function at t0. 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 fluctuation 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
logistic regression with Qj.star.t + 1 as outcome (also needed as a
column in wideDataList) with offset qlogis(Qj.star.t) and
number of additional covariates given by length(trtOfInterest). These
additional covariates should be columns in the each data.frame in
wideDataList called H.z.t where z corresponds to a each
unique value of trtOfInterest. The function returns the same
wideDataList, but with a column called Qj.star.t added to it,
which is the fluctuated initial regression estimate evaluated at the observed
data points.
fluctuateIteratedMean(wideDataList, t, uniqtrt, whichJ, allJ, t0,
Gcomp = FALSE, bounds = NULL, ...)A list of data.frame objects.
The timepoint at which to compute the iterated mean.
The values of trtOfInterest passed to mean_tmle.
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.
A boolean indicating whether mean_tmle was called to
evaluate the G-computation estimator, in which case this function does
nothing but re-label columns.
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.star.t
added to it, which is the fluctuated conditional mean of
Qj.star.t+1 evaluated at the each of the rows of each
data.frame in wideDataList.