Doubly robust estimator of the average treatment effect between two treatments, which is the rate ratio for count outcomes. Bootstrap is used for inference.
atefitcount(
data,
cate.model,
ps.model,
ps.method = "glm",
minPS = 0.01,
maxPS = 0.99,
interactions = TRUE,
n.boot = 500,
seed = NULL,
verbose = 0
)Return an item of the class atefit with the following
elements:
log.rate.ratio: A vector of numeric values of the estimated
ATE (expressed as a log rate ratio of trt=1 over trt=0),
the bootstrap standard error, the lower and upper limits of 95% confidence
interval, and the p-value.
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.
trt.boot: Estimated log rate ratios in each bootstrap
sample.
warning: A warning message produced if the treatment
variable was not coded as 0 or 1. The key to map the original coding of the
variable to a 0-1 coding is displayed in the warning to facilitate the
interpretation of the remaining of the output.
A data frame containing the variables in the outcome, propensity
score, and inverse probability of censoring models (if specified); a data
frame with n rows (1 row per observation).
A formula describing the outcome model to be fitted. The outcome must appear on the left-hand side.
A formula describing the propensity score (PS) model to be
fitted. The treatment must appear on the left-hand side. The treatment must
be a numeric vector coded as 0 or 1. If data are from a randomized controlled
trial, specify ps.model = ~1 as an intercept-only model.
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 treatment-covariate interaction by x. If TRUE,
interactions will be assumed only if at least 10 patients received each
treatment option. Default is TRUE.
A numeric value indicating the number of bootstrap samples
used. Default is 500.
An optional integer specifying an initial randomization seed for
reproducibility. Default is NULL, corresponding to no seed.
An integer value indicating whether intermediate progress
messages should be printed. 1 indicates messages are printed and
0 otherwise. Default is 0.
This helper function estimates the average treatment effect (ATE) between two treatment groups in a given dataset. The ATE is estimated with a doubly robust estimator that accounts for imbalances in covariate distributions between the two treatment groups with inverse probability treatment weighting. For count outcomes, the estimated ATE is the estimated rate ratio between treatment 1 versus treatment 0.
output <- atefitcount(data = countExample,
cate.model = y ~ age + female + previous_treatment +
previous_cost + previous_number_relapses +
offset(log(years)),
ps.model = trt ~ age + previous_treatment,
verbose = 1, n.boot = 50, seed = 999)
output
plot(output)
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