trReg | R Documentation |
trReg
fits transformation model under dependent truncation and independent censoring via a structural transformation model.
trReg(
formula,
data,
subset,
tFun = "linear",
method = c("kendall", "adjust"),
B = 0,
control = list()
)
formula |
a formula expression, of the form |
data |
an optional data frame in which to interpret the variables occurring
in the |
subset |
an optional vector specifying a subset of observations to be used in the fitting process. |
tFun |
a character string specifying the transformation function or a user specified function indicating the relationship between
|
method |
a character string specifying how the transformation parameter is estimated. The available options are |
B |
a numerical value specifies the bootstrap size for estimating the standard error.
When |
control |
a list of control parameters. The following arguments are allowed:
|
The main assumption on the structural transformation model is that it assumes there is a latent, quasi-independent truncation time that is associated with the observed dependent truncation time, the event time, and an unknown dependence parameter through a specified function. The structure of the transformation model is of the form:
h(U) = (1 + a)^{-1} \times (h(T) + ah(X)),
where T
is the truncation time, X
is the observed failure time,
U
is the transformed truncation time that is quasi-independent from X
and h(\cdot)
is a monotonic transformation function.
The condition, T < X
, is assumed to be satisfied.
The quasi-independent truncation time, U
, is obtained by inverting the test for quasi-independence by one of the following methods:
method = "kendall"
by minimizing the absolute value of the restricted inverse probability weighted Kendall's tau or maximize the corresponding p
-value.
This is the same procedure used in the trSUrvfit()
function.
method = "adjust"
includes a function of latent truncation time, U
, as a covariate.
A piece-wise function is constructed based on (Q + 1
) indicator functions on whether U
falls in the Q
th and the (Q+1
)th percentile,
where Q
is the number of cutpoints used. See control
for details.
The transformation parameter, a
, is then chosen to minimize the significance of the coefficient parameter.
A trReg
object containing the following components:
PE
A named numeric matrix of point estimates and related statistics (e.g., coefficient, exponentiated coefficient, standard error, z-score, and p-value).
varNames
Character string giving the name(s) of the covariates.
SE
A numeric vector contains the bootstrap standard error.
a
Estimated transformation parameter.
Call
The matched call to the fitting function.
B
, Q
, P
Model parameters; B
is the bootstrap sapmle, Q
is the number of cutpoints, and P
is the number of break points. See Details.
tFun
A function defining the transformation model.
vNames
Character vector of covariate names.
method
Character string specifying the estimation method (e.g., "kendall"
or "adjust"
).
.data
A data frame used in fitting.
trSurvfit
data(channing, package = "boot")
chan <- subset(channing, entry < exit)
trReg(Surv(entry, exit, cens) ~ sex, data = chan)
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