# ahrWKM: ahrWKM In AHR: Estimation and Testing of Average Hazard Ratios

## Description

Estimate average hazard ratios from k independent samples based on the weighted Kaplan-Meier (WKM) estimator

## Usage

 ```1 2 3``` ```ahrWKM(L, formula, data, null.theta = NULL, contrast = NULL, multi.test = FALSE, cov = TRUE, bootstrap = 0, alpha = 1, left.limit = FALSE, rr.subset = rep(TRUE, nrow(data))) ```

## 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) `alpha` exponent of the weight function `left.limit` if TRUE use left-continuous interpolation of WKM estimates instead of right-continuous interpolation `rr.subset` logical vector defining subset of observations to use for response rate estimation (default: use all observations)

## 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.

`wkm`
 ``` 1 2 3 4 5 6 7 8 9 10 11 12``` ```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 <- ahrWKM(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 <- ahrWKM(2, Surv(Y, D) ~ Z, data.frame(Y=Y, D=D, Z=Z), cov=FALSE, bootstrap=1000) ## End(Not run) ```