hr.comp2: Function to statistically compare two hazard ratios...
In bhklab/survcomp: Performance Assessment and Comparison for Survival Analysis

Description Usage Arguments Details Value Author(s) References See Also Examples

This function compares two hazard ratios from their betas and standard errors as computed by a Cox model for instance. The statistical test is a Student t test for dependent samples. The two hazard ratios must be computed from the same survival data.

1	hr.comp2(x1, beta1, se1, x2, beta2, se2, n)

`x1`	risk score used to estimate the first hazard ratio.
`beta1`	beta estimate for the first hazard ratio.
`se1`	standard error of beta estimate for the first hazard ratio.
`x2`	risk score used to estimate the second hazard ratio.
`beta2`	beta estimate for the second hazard ratio.
`se2`	standard error of beta estimate for the first hazard ratio.
`n`	number of samples from which the hazard ratios were estimated.

The two hazard ratios must be computed from the same samples (and corresponding survival data). The function uses a Student t test for dependent samples.

`p.value`	p-value from the Student t test for the comparison beta1 > beta2 (equivalently hr1 > hr2)
`hr1`	value of the first hazard ratio
`hr2`	value of the second hazard ratio

Benjamin Haibe-Kains

Student 1908) "The Probable Error of a Mean", Biometrika, 6, 1, pages 1–25.

Haibe-Kains, B. and Desmedt, C. and Sotiriou, C. and Bontempi, G. (2008) "A comparative study of survival models for breast cancer prognostication based on microarray data: does a single gene beat them all?", Bioinformatics, 24, 19, pages 2200–2208.

coxph, t.test

set.seed(12345)
age <- as.numeric(rnorm(100, 50, 10) >= 50)
size <- as.numeric(rexp(100,1) > 1)
stime <- rexp(100)
cens <- runif(100,.5,2)
sevent <- as.numeric(stime <= cens)
stime <- pmin(stime, cens)
coxm1 <- coxph(Surv(stime, sevent) ~ age)
coxm2 <- coxph(Surv(stime, sevent) ~ size)
hr.comp2(x1=age, beta1=coxm1$coefficients, se1=drop(sqrt(coxm1$var)),
  x2=size, beta2=coxm2$coefficients, se2=drop(sqrt(coxm2$var)), n=100)