surv_aiptw | R Documentation |
This page explains the details of estimating augmented inverse probability of treatment weighted survival curves for single event time-to-event data (method="aiptw"
in the adjustedsurv
function). All regular arguments of the adjustedsurv
function can be used. Additionally, the outcome_model
argument and the treatment_model
argument have to be specified in the adjustedsurv
call. Further arguments specific to this method are listed below.
outcome_model |
[required] Must be a |
treatment_model |
[required] Must be a |
censoring_model |
Must be a |
verbose |
Whether to print estimation information of the |
... |
Further arguments passed to |
Type of Adjustment: Requires both a treatment assignment model (glm
) and a outcome model (coxph
). Also allows, but does not rely on, an additional model describing the censoring mechanism (also a coxph
object).
Doubly-Robust: Estimates are Doubly-Robust.
Categorical groups: Currently only two groups in variable
are allowed. Must still be a factor variable.
Approximate Variance: Calculations to approximate the variance and confidence intervals are available.
Allowed Time Values: Allows both continuous and integer time.
Bounded Estimates: Estimates are not guaranteed to be bounded in the 0 to 1 probability range.
Monotone Function: Estimates are not guaranteed to be monotone.
Dependencies: This method relies on the riskRegression package.
Instead of only modeling the outcome mechanism or the treatment assignment mechanism, both kind of models are required to use this method. If either of those models are correctly specified, unbiased estimates will be obtained. Can also be used to adjust for dependent censoring using another Cox-Regression model. An obvious advantage of this method is it's doubly robust property. This however comes at the price of some efficiency. It is also possible that some estimates fall outside the 0 and 1 probability bounds, particularly if the time is near 0 or the maximal observed event time. There is also no guarantee that the estimated survival curves will be monotonically decreasing. For more information on the methods the user is referred to the literature listed in the references.
This function is basically just a wrapper around the ate
function from the riskRegression package. Additional arguments may be passed to that function using the ...
syntax. It is however recommended to use ate
directly in these cases.
Adds the following additional objects to the output of the adjustedsurv
function:
ate_object
: The object returned by the ate
function.
The wrapper function was written by Robin Denz, the ate
function (which this wrapper is build around) was written by other people. See ?ate
for more details.
James M. Robins and Andrea Rotnitzky (1992). "Recovery of Information and Adjustment for Dependent Censoring Using Surrogate Markers". In: AIDS Epidemiology: Methodological Issues. Ed. by Nicholas P. Jewell, Klaus Dietz, and Vernon T. Farewell. New York: Springer Science + Business Media, pp. 297-331
Alan E. Hubbard, Mark J. van der Laan, and James M. Robins (2000). "Nonparametric Locally Efficient Estimation of the Treatment Specific Survival Distribution with Right Censored Data and Covariates in Observational Studies". In: Statistical Models in Epidemiology, the Environment, and Clinical Trials. Ed. by M. Elizabeth Halloran and Donald Berry. New York: Springer Science + Business Media, pp. 135-177
Min Zhang and Douglas E. Schaubel (2012). "Contrasting Treatment-Specific Survival Using Double-Robust Estimators". In: Statistics in Medicine 31.30, pp. 4255-4268
Xiaofei Bai, Anastasios A. Tsiatis, and Sean M. O’Brien (2013). "Doubly-Robust Estimators of Treatment-Specific Survival Distributions in Observational Studies with Stratified Sampling". In: Biometrics 69, pp. 830–839
Brice Maxime Hugues Ozenne, Thomas Harder Scheike, and Laila Staerk (2020). "On the Estimation of Average Treatment Effects with Right-Censored Time to Event Outcome and Competing Risks". In: Biometrical Journal 62, pp. 751–763
ate
, coxph
, glm
library(adjustedCurves)
library(survival)
if (requireNamespace("riskRegression")) {
library(riskRegression)
set.seed(42)
# simulate some data as example
sim_dat <- sim_confounded_surv(n=50, max_t=1.2)
sim_dat$group <- as.factor(sim_dat$group)
# estimate a cox-regression for the outcome
cox_mod <- coxph(Surv(time, event) ~ x1 + x2 + x3 + x4 + x5 + x6 + group,
data=sim_dat, x=TRUE)
# estimate a treatment assignment model
glm_mod <- glm(group ~ x1 + x3 + x5 + x6, data=sim_dat, family="binomial")
# use it to calculate adjusted survival curves
adjsurv <- adjustedsurv(data=sim_dat,
variable="group",
ev_time="time",
event="event",
method="aiptw",
outcome_model=cox_mod,
treatment_model=glm_mod,
conf_int=FALSE)
# plot the curves
plot(adjsurv)
}
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