Description Usage Arguments Value Examples
This function estimates the marginal cumulative incidence for failures of specified types using targeted minimum loss-based estimation.
1 2 3 4 5 6 7 8 | survtmle(ftime, ftype, trt, adjustVars, t0 = max(ftime[ftype > 0]),
SL.ftime = NULL, SL.ctime = NULL, SL.trt = NULL,
glm.ftime = NULL, glm.ctime = NULL, glm.trt = NULL,
returnIC = TRUE, returnModels = TRUE,
ftypeOfInterest = unique(ftype[ftype != 0]),
trtOfInterest = unique(trt), method = "hazard", bounds = NULL,
verbose = FALSE, tol = 1/(sqrt(length(ftime))), maxIter = 10,
Gcomp = FALSE, gtol = 0.001)
|
ftime |
An integer-valued vector of failure times. Right-censored observations
should have corresponding |
ftype |
An integer-valued vector indicating the type of failure. Observations
with |
trt |
A numeric vector indicating observed treatment assignment. Each unique value will be treated as a different type of treatment. Currently, only two unique values are supported. |
adjustVars |
A data.frame of adjustment variables that will be used in estimating the conditional treatment, censoring, and failure (hazard or conditional mean) probabilities. |
t0 |
The time at which to return cumulative incidence estimates. By
default this is set to |
SL.ftime |
A character vector or list specification to be passed to the
|
SL.ctime |
A character vector or list specification to be passed to the
|
SL.trt |
A character vector or list specification to be passed to the
|
glm.ftime |
A character specification of the right-hand side of the
equation passed to the |
glm.ctime |
A character specification of the right-hand side of the
equation passed to the |
glm.trt |
A character specification of the right-hand side of the
equation passed to the |
returnIC |
A boolean indicating whether to return vectors of influence
curve estimates. These are needed for some post-hoc comparisons, so it
is recommended to leave as |
returnModels |
A boolean indicating whether to return the
|
ftypeOfInterest |
An input specifying what failure types to compute
estimates of incidence for. The default value computes estimates for
values |
trtOfInterest |
An input specifying which levels of |
method |
A character specification of how the targeted minimum
loss-based estimators should be computed, either |
bounds |
A |
verbose |
A boolean indicating whether the function should print
messages to indicate progress. If |
tol |
The stopping criteria when |
maxIter |
A maximum number of iterations for the algorithm when
|
Gcomp |
A boolean indicating whether to compute the G-computation
estimator (i.e., a substitution estimator with no targeting step).
Theory does not support inference for the Gcomp estimator if Super
Learner is used to estimate failure and censoring distributions. The
G-computation is only implemented for |
gtol |
The truncation level of predicted censoring survival. Setting to larger values can help performance in data sets with practical positivity violations. |
An object of class survtmle
.
The call to survtmle
.
A numeric vector of point estimates – one for each combination of
ftypeOfInterest
and trtOfInterest
.
A covariance matrix for the point estimates.
The empirical mean of the efficient influence function at the estimated, targeted nuisance parameters. Each value should be small or the user will be warned that excessive finite-sample bias may exist in the point estimates.
The efficient influence function at the estimated, fluctuated nuisance parameters, evaluated on each of the observations. These are used to construct confidence intervals for post-hoc comparisons.
If returnModels=TRUE
the fit object(s) for the call to
glm
or SuperLearner
for the outcome regression
models. If method="mean"
this will be a list of length
length(ftypeOfInterest)
each of length t0
(one
regression for each failure type and for each timepoint). If
method="hazard"
this will be a list of length
length(ftypeOfInterest)
with one fit corresponding to
the hazard for each cause of failure. If
returnModels = FALSE
, this entry will be NULL
.
If returnModels=TRUE
the fit object for the call to
glm
or SuperLearner
for the pooled hazard
regression model for the censoring distribution. If
returnModels=FALSE
, this entry will be NULL
.
If returnModels = TRUE
the fit object for the call to
glm
or SuperLearner
for the conditional
probability of trt
regression model. If
returnModels = FALSE
, this entry will be NULL
.
The timepoint at which the function was evaluated.
The numeric vector of failure times used in the fit.
The numeric vector of failure types used in the fit.
The numeric vector of treatment assignments used in the fit.
The data.frame of failure times used in the fit.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 | # simulate data
set.seed(1234)
n <- 200
trt <- rbinom(n, 1, 0.5)
adjustVars <- data.frame(W1 = round(runif(n)), W2 = round(runif(n, 0, 2)))
ftime <- round(1 + runif(n, 1, 4) - trt + adjustVars$W1 + adjustVars$W2)
ftype <- round(runif(n, 0, 1))
# Fit 1
# fit a survtmle object with glm estimators for treatment, censoring, and
# failure using the "mean" method
fit1 <- survtmle(ftime = ftime, ftype = ftype,
trt = trt, adjustVars = adjustVars,
glm.trt = "W1 + W2",
glm.ftime = "trt + W1 + W2",
glm.ctime = "trt + W1 + W2",
method = "mean", t0 = 6)
fit1
# Fit 2
# fit an survtmle object with SuperLearner estimators for failure and
# censoring and empirical estimators for treatment using the "mean" method
fit2 <- survtmle(ftime = ftime, ftype = ftype,
trt = trt, adjustVars = adjustVars,
SL.ftime = c("SL.mean"),
SL.ctime = c("SL.mean"),
method = "mean", t0 = 6)
fit2
|
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