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

In bhm: Biomarker Threshold Models

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 class '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)
#

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).

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.

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

Note

Based on code from Parisa Gavanji.

Author(s)

Bingshu E. Chen ([e-mail])

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) 
#

bhm documentation built on Sept. 1, 2022, 1:10 a.m.