Description Usage Arguments Details See Also Examples
trReg
fits transformation model under dependent truncation and independent censoring via a structural transformation model.
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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 X, T, and a.
When

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, quasiindependent truncation time that is associated with the observed dependent truncation time, the event time, and an unknown dependence parameter through a specified funciton. 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 quasiindependent from X and h(\cdot) is a monotonic transformation function. The condition, T < X, is assumed to be satisfied. The quasiindependent truncation time, U, is obtained by inverting the test for quasiindependence 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 pvalue.
This is the same procedure used in the trSUrvfit()
function.
method = "adjust"
includes a function of latent truncation time, U, as a covariate.
A piecewise function is constructed based on (Q + 1) indicator functions on whether U falls in the Qth 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.
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