icedr: Double Robust Sequential Regression Estimation

In tranlm/lrecCompare: Comparative methods in the estimation of a treatment specific mean outcome

Description

icedr Estimates the parameter of interest (E[Y_d]) by using the sequential regression approach as presented by Bang and Robins (2005). This function only works in the setting where the outcome is binary or the outcome is continuous with a censoring intervention

Usage

icedr(data, Ynodes, Anodes, Cnodes = NULL, abar, cum.g, Qform,
  SL.library = NULL, family = "quasibinomial", g.weight = FALSE,
  stratify = TRUE)

Arguments

data	data frame following the time-ordering of the nodes.
Ynodes	column names or indicies in data of outcome nodes.
Anodes	column names or indicies in data of treatment nodes.
Cnodes	column names or indicies in data of censoring nodes.
abar	binary vector (numAnodes x 1) of counterfactual treatment
cum.g	a matrix of the cumulative probabilities of treatment (and being uncensored) given the parents.
Qform	character vector of regression formulas for Qbar.
SL.library	optional character vector of libraries to pass to SuperLearner. NULL indicates glm should be called instead of SuperLearner.
family	Currently allows gaussian or binomial to describe the error distribution. Link function information will be ignored.
g.weight	if TRUE, then use the g fits as weights in the Qbar fit, rather than as a covariate.
stratify	if TRUE condition on following abar when estimating Qbar. If FALSE, pool over all subjects.

Value

icedr returns a list of items as an object of class icedr, which include

• The estimate of the parameter value under the intervention abar

• A matrix of the condition expectation fit Qbar under abar for each time point.

• The conditional expectation fits for Qbar

• The call to the function

tranlm/lrecCompare documentation built on May 31, 2019, 7:44 p.m.