# tranSurvfit: This function creates survival curves under dependent... In tranSurv: Estimating a Survival Distribution in the Presence of Dependent Left Truncation and Right Censoring

## Description

A structural transformation model for a latent, quasi-independent truncation time as a function of the observed dependent truncation time and the event time, and an unknown dependence parameter. The dependence parameter is chosen to either minimize the absolute value of the restricted inverse probability weighted Kendall's tau or maximize the corresponding p-value. The marginal distribution for the truncation time and the event time are completely left unspecified.

## Usage

 ```1 2``` ```tranSurvfit(trun, obs, delta = NULL, trans = "linear", plots = FALSE, control = tranSurv.control(), ...) ```

## Arguments

 `trun` left truncation time, satisfying trun <= obs. `obs` observed failure time, must be the same length as `trun`, might be right-censored. `delta` an optional vector of censoring indicator (0 = censored,1 = event) for obs. When this vector is not specified, the function assumes there is no censoring and all observed failure time experienced an event. `trans` a character string specifying the transformation structure. The following are permitted: `linear`: linear transformation structure, `log`: log-linear transformation structure, `exp`: exponential transformation structure. `plots` an optional logical value; if TRUE, a series of diagnostic plots as well as the survival curve for the observed failure time will be plotted. `control` controls lower and upper bounds when `trans` is an user specified function. `...` for future methods.

## Details

The structure of the transformation model is of the form:

h(U) = (1 + a)^-1 * (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(.) 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 either minimizing the absolute value of the restricted inverse probability weighted Kendall's tau or maximize the corresponding p-value.

At the current version, three transformation structures can be specified. `trans = "linear"` corresponds to h(X) = 1; `trans = "log"` corresponds to h(X) = log(X); ```trans = "exp"``` corresponds to h(X) = exp(X).

## Value

The output contains the following components:

 `Sy` estiamted survival function at the (ordered) observed points. `byTau` a list contains the estimator of transformation parameter. The following are the components: `par`: the best set of transformation parameter found. `obj`: the value of the inverse probability weighted Kendall's tau corresponding to 'par'. `byP` a list contains the estimator of transformation parameter. The following are the components: `par`: the best set of transformation parameter found. `obj`: the value of p-value based on the inverse probability weighted Kendall's tau corresponding to 'par'. `qind` a data frame consists of two quasi-independent variables: `trun`: the transformed truncation time. `obs`: the corresponding uncensored failure time.

## References

Martin E. and Betensky R. A. (2005), Testing quasi-independence of failure and truncation times via conditional Kendall's tau, Journal of the American Statistical Association, 100 (470): 484-492.

Austin, M. D. and Betensky R. A. (2014), Eliminating bias due to censoring in Kendall's tau estimators for quasi-independence of truncation and failure, Computational Statistics & Data Analysis, 73: 16-26.

Chiou, S., Austin, M., Qian, J. and Betensky R. A. (2016), Transformation model estimation of survival under dependent truncation and independent censoring, an unpublished manuscript.

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24``` ```## Generate simulated data from a transformation model datgen <- function(n) { a <- -0.3 X <- rweibull(n, 2, 4) ## failure times U <- rweibull(n, 2, 1) ## latent truncation time T <- (1 + a) * U - a * X ## apply transformation C <- rlnorm(n, .8, 1) ## censoring dat <- data.frame(trun = T, obs = pmin(X, C), delta = 1 * (X <= C)) return(subset(dat, trun <= obs)) } set.seed(123) dat <- datgen(300) fit <- with(dat, tranSurvfit(trun, obs, delta)) fit ## Checking the transformation parameter fit\$byTau\$par fit\$byTau\$obj with(dat, condKendall(trun, obs, delta, method = "IPW2", a = fit\$byTau\$par))\$PE fit\$byP\$par fit\$byP\$obj with(dat, condKendall(trun, obs, delta, method = "IPW2", a = fit\$byP\$par))\$p.value ```

tranSurv documentation built on May 30, 2017, 8:19 a.m.