bhm (version 1.11)

pIndex: Probability Index for Survival Time Difference

Description

{pIndex} is a function to estimate and test differce of survival time among groups. It is defined as p = Pr{T_1 < T_2 }, where $T_1$ is survival time for subjects in group 1 and $T_2$ is surval time in group 2.

Usage

pIndex(x, ...)
# S3 method for formula
pIndex(formula, data, control = list(...),...)
###To estimate probability index for treatment and control groups (define by trt):
#
# fit = pIndex(Surv(time, status) ~ trt)
#
###To estimate probability index difference for treatment and control 
###groups (define by trt) between biomarker postive and biomarker negative
###subjects(i.e. Treatment-biomarker interaction):
#
# fit = pIndex(Surv(time, status) ~ trt+biomarker)
#

Arguments

formula

an object of class "formula"(or one that can be coerced to that class): a sy mbolic description of the model to be fitted. The details of model specification are given under 'Details'.

data

an optional data frame, list or environment (or object coercible by 'as.data.frame' to a data frame) containing the variables in the model. If not found in data, the variables are taken from environment(formula), typically the enviro nment from which pIndex is called.

x

Here covariate x is a design matrix of dimension n * 1 (for two sample test) or dimension n * 2 (for treatment * biomarker interaction).

control

a list of parameters for controlling the fitting process. See 'pIndexControl' for details

…

additional arguments to be passed to the low level regression fitting functions (see below).

Value

pIndex returns an object of class inheriting from "pIndex". See later in this section.

Details

pIndex(y~x) will estimate probability index of two groups (eg. treatment vs control) define by x. pIndex(y~x1 + x2) will estimate the difference of probability index of x1 (eg. treatment vs control) between biomarker positive and biomarker negative groups (x2). Function print(x) can be used to print a summary of pIndex results.

References

Jiang, S., Chen, B. E. and Tu, D.(2016). Inference on treatment-covariate interaction based on a nonparametric measure of treatment effects and censored survival data. Statistics in Medicine. 35, 2715-2725.

See Also

bhm, pIndexControl,

Examples

# NOT RUN {
##
## Generate a random data set
n = 300
b = c(0.5, 1, 1.5)
data = surv.gendat(n, c0 = 0.40, beta = b)
age = runif(n, 0, 1)*100
tm = data[, 1]
status = data[, 2]
trt = data[, 3]
ki67 = data[, 4]
#
### No run
# 
# fit = pIndex(Surv(tm, status) ~ trt + ki67) 
#
# }