AHR (version 1.5.0)

AHR: AHR

Description

This package provides methods for estimation of multivariate average hazard ratios as defined by Kalbfleisch and Prentice. The underlying survival functions of the event of interest in each group can be estimated using either the (weighted) Kaplan-Meier estimator or the Aalen-Johansen estimator for the transition probabilities in Markov multi-state models. Right-censored and left-truncated data is supported. Moreover, the difference in restricted mean survival can be estimated. Currently variance estimation for the average hazard ratio based on the Aalen-Johansen estimator is only supported for competing risks models, i.e. for estimation of the average sub-distribution hazard ratio (Average cause-specific hazard ratios can be estimated by using the Kaplan-Meier estimator with competing risks data).

Details

Furthermore estimation of quantiles, ratios and differences of quantiles and corresponding p-values and confidence intervals of survival times based on the (weighted) Kaplan-Meier estimator and the Aalen-Johansen estimator is also supported.

References

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

S.~Murray and A.~A. Tsiatis. Nonparametric survival estimation using prognostic longitudinal covariates. Biometrics, 52(1):137--151, Mar. 1996.

C.~A. Struthers and J.~D. Kalbfleisch. Misspecified proportional hazard models. Biometrika, 73(2):363--369, Aug. 1986.

Examples

# NOT RUN {
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))

## uses Kaplan-Meier estimator by default
fit <- avgHR(2, data.frame(Y=Y, D=D, Z=Z), formula=Surv(Y, D) ~ Z)
fit

## same as
# }
# NOT RUN {
fit <- avgWKM(2, data.frame(Y=Y, D=D, Z=Z), formula=Surv(Y, D) ~ Z)
# }
# NOT RUN {
## use bootstrap to estimate covariance matrix
# }
# NOT RUN {
fit <- avgWKM(2, data.frame(Y=Y, D=D, Z=Z), formula=Surv(Y, D) ~ Z, cov=FALSE,
                       bootstrap=10000)
# }
# NOT RUN {
## calculate restricted mean difference
rdm <- rmeanDiff.ahr(fit)
rdm

## ventilation status in intensive care unit patients dataset from etm package
library(etm)
data(sir.cont)
df <- sir.cont
df$Trt <- factor(rep(0, nrow(df)), levels=c(0, 1))
ids <- unique(df$id)
df$Trt[df$id %in% sample(ids, floor(length(ids)/2), FALSE)] <- 1

# transition matrix
tra <- matrix(FALSE, nrow=3, ncol=3)
tra[1, 2:3] <- TRUE
tra[2, c(1, 3)] <- TRUE

# NOTE: variance estimation not yet supported for Aalen-Johansen based avg. HR
sc.fit <- avgHR(2, method="aj", data=df, target="0 2", states=c("0", "1", "2"), transitions=tra,
                censoring="cens", cov=FALSE)
sc.fit

# }