Description Usage Arguments Value Examples
This function estimates the marginal cumulative incidence for failures of
specified types using targeted minimum loss-based estimation based on the
G-computation representation of cumulative incidence. The function is called
by survtmle
whenever method = "mean"
is specified. However,
power users could, in theory, make calls directly to this function.
1 2 3 4 5 6 7 | mean_tmle(ftime, ftype, trt, t0 = max(ftime[ftype > 0]),
adjustVars = NULL, SL.ftime = NULL, SL.ctime = NULL,
SL.trt = NULL, glm.ftime = NULL, glm.ctime = NULL, glm.trt = "1",
glm.family = "binomial", returnIC = TRUE, returnModels = FALSE,
ftypeOfInterest = unique(ftype[ftype != 0]),
trtOfInterest = unique(trt), bounds = NULL, verbose = FALSE,
Gcomp = FALSE, gtol = 0.001, ...)
|
ftime |
A numeric vector of failure times. Right-censored observations
should have corresponding |
ftype |
A numeric 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. |
t0 |
The time at which to return cumulative incidence estimates. By
default this is set to |
adjustVars |
A |
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 |
glm.family |
The type of regression to be performed if fitting GLMs in
the estimation and fluctuation procedures. The default is "binomial"
for logistic regression. Only change this from the default if there
are justifications that are well understood. This is passed directly
to |
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 |
bounds |
A |
verbose |
A boolean indicating whether the function should print
messages to indicate progress. If |
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 G-computation estimator if
Super Learner is used to estimate failure and censoring distributions.
The G-computation is only implemented if |
gtol |
The truncation level of predicted censoring survival. Setting to larger values can help performance in data sets with practical positivity violations. |
... |
Other options. Not currently used. |
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 | ## Single failure type examples
# simulate data
set.seed(1234)
n <- 100
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 mean_tmle object with GLMs for treatment, censoring, failure
fit1 <- mean_tmle(ftime = ftime, ftype = ftype,
trt = trt, adjustVars = adjustVars,
glm.trt = "W1 + W2",
glm.ftime = "trt + W1 + W2",
glm.ctime = "trt + W1 + W2")
|
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