Doubly robust estimator of the average treatment effect between two treatments, which is the mean difference of treatment 1 over treatment 0 for continuous outcomes.
drmean(
y,
trt,
x.cate,
x.ps,
ps.method = "glm",
minPS = 0.01,
maxPS = 0.99,
interactions = TRUE
)Return a list of 4 elements:
mean.diff: A numeric value of the estimated mean difference.
mean.diff0: A numeric value of the estimated mean difference
in treatment group 0.
mean.diff1: A numeric value of the estimated mean difference
in treatment group 1.
A numeric vector of size n with each element representing
the observed continuous outcome for each subject.
A numeric vector (in {0, 1}) of size n with each element
representing the treatment received for each subject.
A numeric matrix of dimension n by p.cate with
each column representing each baseline covariate specified in the outcome
model for all subjects.
A numeric matrix of dimension n by p.ps + 1 with
a leading column of 1 as the intercept and each remaining column representing
each baseline covariate specified in the propensity score model for all
subjects
A character value for the method to estimate the propensity
score. Allowed values include one of:
'glm' for logistic regression with main effects only (default), or
'lasso' for a logistic regression with main effects and LASSO
penalization on two-way interactions (added to the model if interactions are
not specified in ps.model). Relevant only when ps.model has
more than one variable.
A numerical value between 0 and 1 below which estimated propensity
scores should be truncated. Default is 0.01.
A numerical value between 0 and 1 above which estimated propensity
scores should be truncated. Must be strictly greater than minPS.
Default is 0.99.
A logical value indicating whether the outcome model
should assume interactions between x and trt. If TRUE,
interactions will be assumed only if at least 10 patients received each
treatment option. Default is TRUE.