Doubly robust estimator of the average treatment effect between two treatments, which is the rate ratio of treatment 1 over treatment 0 for count outcomes.
drcount(
y,
trt,
x.cate,
x.ps,
time,
ps.method = "glm",
minPS = 0.01,
maxPS = 0.99,
interactions = TRUE
)Return a list of 4 elements:
log.rate.ratio: A numeric value of the estimated log rate ratio.
rate0: A numeric value of the estimated rate in the group trt=0.
rate1: A numeric value of the estimated rate in the group trt=1.
A numeric vector of size n with each element representing
the observed count 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 numeric vector of size n with each element representing
the log-transformed person-years of follow-up for each subject.
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 allow for treatment-covariate interaction by x. If TRUE,
interactions will be assumed only if at least 10 patients received each
treatment option. Default is TRUE.