estimateIteratedMean: Estimation for the Method of Iterated Means

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

Description

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.

Usage

estimateIteratedMean(wideDataList, t, whichJ, allJ, t0, adjustVars,
  SL.ftime = NULL, glm.ftime = NULL, verbose, returnModels = FALSE,
  bounds = NULL, ...)

Arguments

wideDataList	A list of data.frame objects.
t	The timepoint at which to compute the iterated mean.
whichJ	Numeric value indicating the cause of failure for which regression should be computed.
allJ	Numeric vector indicating the labels of all causes of failure.
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.
adjustVars	Object of class data.frame that contains the variables to adjust for in the regression.
SL.ftime	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.ftime	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).
verbose	A boolean indicating whether the function should print messages to indicate progress.
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.
bounds	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.

Value

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.

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