atefit {precmed} | R Documentation |
Doubly robust estimator of and inference for the average treatment effect for count, survival and continuous data
Description
Doubly robust estimator of the average treatment effect between two treatments, which is the rate ratio for count outcomes, the restricted mean time lost ratio for survival outcomes and the mean difference for continuous outcome. Bootstrap is used for inference.
Usage
atefit(
response,
data,
cate.model,
ps.model,
ps.method = "glm",
ipcw.model = NULL,
ipcw.method = "breslow",
minPS = 0.01,
maxPS = 0.99,
followup.time = NULL,
tau0 = NULL,
surv.min = 0.025,
interactions = TRUE,
n.boot = 500,
seed = NULL,
verbose = 0
)
Arguments
response |
A string describing the type of outcome in the data. Allowed values include
"count" (see |
data |
A data frame containing the variables in the outcome, propensity score, and inverse
probability of censoring models (if specified); a data frame with |
cate.model |
A formula describing the outcome model to be fitted.
The outcome must appear on the left-hand side. For survival outcomes,
a |
ps.model |
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 0/1.
If data are from a randomized controlled trial, specify |
ps.method |
A character value for the method to estimate the propensity score.
Allowed values include one of:
|
ipcw.model |
A formula describing the inverse probability of censoring weighting (IPCW)
model to be fitted. The left-hand side must be empty. Only applies for survival outcomes.
Default is |
ipcw.method |
A character value for the censoring model. Only applies for survival
outcomes. Allowed values are: |
minPS |
A numerical value (in [0, 1]) below which estimated propensity scores should be
truncated. Default is |
maxPS |
A numerical value (in (0, 1]) above which estimated propensity scores should be
truncated. Must be strictly greater than |
followup.time |
A column name in |
tau0 |
The truncation time for defining restricted mean time lost. Only applies for
survival outcomes. Default is |
surv.min |
Lower truncation limit for the probability of being censored.
It must be a positive value and should be chosen close to 0. Only applies for survival
outcomes. Default is |
interactions |
A logical value indicating whether the outcome model should assume interactions
between |
n.boot |
A numeric value indicating the number of bootstrap samples used. Default is |
seed |
An optional integer specifying an initial randomization seed for reproducibility.
Default is |
verbose |
An integer value indicating whether intermediate progress messages and histograms should
be printed. |
Details
For count response, see details in atefitcount()
.
For survival response, see details in atefitsurv()
.
Value
For count response, see description of outputs in atefitcount()
.
For survival response, see description of outputs in atefitsurv()
.
Examples
# Count outcome
output <- atefit(response = "count",
data = countExample,
cate.model = y ~ age + female + previous_treatment +
previous_cost + previous_number_relapses +
offset(log(years)),
ps.model = trt ~ age + previous_treatment,
n.boot = 50,
seed = 999)
output
plot(output)
# Survival outcome
tau0 <- with(survivalExample,
min(quantile(y[trt == "drug1"], 0.95), quantile(y[trt == "drug0"], 0.95)))
output2 <- atefit(response = "survival",
data = survivalExample,
cate.model = survival::Surv(y, d) ~ age + female +
previous_cost + previous_number_relapses,
ps.model = trt ~ age + previous_treatment,
tau0 = tau0,
seed = 999)
output2
plot(output2)