- formula
a formula expression, of the form response ~ predictors.
The response is a binary treatment/exposure variable,
for which a logistic regression model (a Propensity Scores model) will be fit using glm.
See the documentation of glm and formula for details. As an alternative to specifying formula,
arguments A and C, defined below, can be specified.
- data
a data frame that includes a treatment indicator A and covariates C appearing in formula.
- A
a character specifying the name of the treatment/exposure variable.
It is assumed that A is a numeric binary indicator with 0/1 values, where A=1
is assumed a treatment group, and A=0 a control group.
- C
a vector of character strings with variable names (potential confounders)
in the logistic regression model for Propensity Scores, i.e. P(A=1|C=c).
The default value of C is NULL corresponding to wtype="unadj"
that will estimate treatment effects in the raw (observed) data.
- wtype
a character string variable indicating the type of weights that will define the target
population for which the ATE will be estimated.
The default is "unadj" - this will not adjust for possible
treatment selection bias and will not use propensity scores weighting. It can be used, for example,
in data from a randomized controlled trial (RCT) where there is no need for emulation of baseline randomization.
Other possible values are "stab.ATE", "ATE", "ATT", "ATC" and "overlap".
See Table 1 from Li, Morgan, and Zaslavsky (2018).
- case.w
a vector of case weights.