Calculates the cumulative hazard rate (Nelson-Aalen estimator)
Arguments
- data
A data frame containing the data.
- timevar
The name of the time variable in
data
.- statusvar
The name of the event variable, e.g. death in
data
.
Value
A vector with nrow(data)
elements containing the Nelson-Aalen
estimates of the cumulative hazard function.
Details
This function is useful for imputing variables that depend on survival time. White and Royston (2009) suggested using the cumulative hazard to the survival time H0(T) rather than T or log(T) as a predictor in imputation models. See section 7.1 of Van Buuren (2012) for an example.
References
White, I. R., Royston, P. (2009). Imputing missing covariate values for the Cox model. Statistics in Medicine, 28(15), 1982-1998.
Van Buuren, S. (2018). Flexible Imputation of Missing Data. Second Edition. Chapman & Hall/CRC. Boca Raton, FL.
Examples
require(MASS)
#> Loading required package: MASS
#>
#> Attaching package: ‘MASS’
#> The following object is masked from ‘package:dplyr’:
#>
#> select
leuk$status <- 1 ## no censoring occurs in leuk data (MASS)
ch <- nelsonaalen(leuk, time, status)
plot(x = leuk$time, y = ch, ylab = "Cumulative hazard", xlab = "Time")
### See example on http://www.engineeredsoftware.com/lmar/pe_cum_hazard_function.htm
time <- c(43, 67, 92, 94, 149, rep(149, 7))
status <- c(rep(1, 5), rep(0, 7))
eng <- data.frame(time, status)
ch <- nelsonaalen(eng, time, status)
plot(x = time, y = ch, ylab = "Cumulative hazard", xlab = "Time")