ahrKM: ahrKM

In mbrueckner/AHR: Estimation and Testing of Average Hazard Ratios

Description

Estimate average hazard ratios from k independent samples based on the Kaplan-Meier estimator

Usage

ahrKM(L, formula, data, null.theta = NULL, contrast = NULL,
  multi.test = FALSE, cov = TRUE, bootstrap = 0,
  left.limit = FALSE)

Arguments

L	time-limit specifying time-interval [0,L] over which average hazard ratios will be calculated
formula	an object of class '"formula"' specifying the conditional survival model
data	data frame containing the variables in formula
null.theta	vector specifying the null hypothesis for the average hazard ratios (H_0: theta = null.theta)
contrast	vector of contrasts to test H_0: contrast * (theta - null.theta) = 0
multi.test	calculate multivariate test statistic if TRUE
cov	if TRUE calculate covariance matrix estimator (direct)
bootstrap	if > 0 then use bootstrap to estimate covariance matrix (ignore if cov is TRUE)
left.limit	if TRUE use left-continuous interpolation of WKM estimates

Value

An object of class '"ahr"'

References

J.~D. Kalbfleisch and R.~L. Prentice. Estimation of the average hazard ratio. Biometrika, 68(1):105–112, Apr. 1981.

See Also

survfit

Examples

T <- c(rexp(100, 1), rexp(100, 2))
C <- c(rexp(100, 1), rexp(100, 2))
Y <- pmin(T, C)
D <- T <= C
Z <- rep(c(0,1), c(100, 100)) # treatment indicator
fit <- ahrKM(2, Surv(Y, D) ~ Z, data.frame(Y=Y, D=D, Z=Z))
fit

## the same as above, but estimate covariance matrix using bootstrap
## Not run: fitBS <- ahrKM(2, Surv(Y, D) ~ Z, data.frame(Y=Y, D=D, Z=Z), cov=FALSE,
                         bootstrap=1000)
## End(Not run)

mbrueckner/AHR documentation built on May 22, 2019, 12:57 p.m.