bhm (version 1.18)

resboot: Rresidual Bootstrap Test (RBT) for treatment-biomarker interaction

Description

{resboot} is a function to test the existance of treatment-biomarker interaction in biomarker threshold model

g(Y) = b0+b1*I(w>c) + b2*z + b3*I(w>c)*z.

Usage

resboot(x, ...)
# S3 method for formula
resboot(formula, family, data=list(...), B = 100, epsilon = 0.01, ...)
#
###To test the null hypothesis of interaction between treatment variable  
###(define by z) and biomarker variables (define by w) for survival dataa, 
###use:
#
# fit = resboot(Surv(time, status) ~ w + z + w:z)
#

Value

resboot returns an object of class inheriting from "resboot". When B > 0, an object of class "resboot" is a list containing at least the following components:

theta

the estimated maximum of likelihood ratio statistics

theta.b

Bootstrap sample of theta

sd

standard deviation of theta based on resampling

ci

(1-alpha) percent confidence interval for theta based on resampling

Arguments

formula

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

family

default is family = 'Surv' for survival data.

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 resboot 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).

B

Number of bootstraps, default is B = 100

epsilon

Biomarker (transformed) step length for profile likelihood method, default is epsilon = 0.01

...

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

Author

Bingshu E. Chen ([e-mail])

Details

resboot(y~w + z + w:z) will give residual bootstrap p-value for interaction between biomarker variable (w) and treatment variable (z). The null hypothesis is given by H0: b3 = 0, where b3 is the regression coefficient for the interaction term I(w>c)*z. Function print(x) can be used to print a summary of resboot results.

References

Gavanji, P., Chen, B. E. and Jiang, W.(2018). Residual Bootstrap test for interactions in biomarker threshold models with survival data. Statistics in Biosciences.

See Also

bhm coxph

Examples

##
## Generate a random data set
n = 30
b = c(0.5, 1, 1.5)
data = gendat.surv(n, c0 = 0.40, beta = b)
tm = data[, 1]
status = data[, 2]
trt = data[, 3]
ki67 = data[, 4]
#
### No run
# 
# fit = resboot(Surv(tm, status) ~ ki67+trt+ki67:trt) 
#