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muhaz

Smooth hazard function estimation from right-censored survival data.

Overview

muhaz estimates the hazard function from right-censored data using kernel-based methods, implementing the bandwidth selection algorithms and boundary kernel formulations described in Mueller and Wang (1994). Options include:

Three bandwidth methods: local MSE-optimal, global IMSE-optimal, and k-nearest-neighbor
Three boundary correction types: none, left only, or both ends
Four kernel shapes: rectangle, Epanechnikov, biquadratic, triquadratic

A complementary set of piecewise-exponential estimators (pehaz, plot.pehaz) is also provided for quick exploratory comparison.

Original S code by Kenneth Hess (M.D. Anderson Cancer Center); R port by R. Gentleman. Currently maintained by David Winsemius.

Installation

Install the released version from CRAN:

install.packages("muhaz")

Install the development version from GitHub:

# install.packages("remotes")
remotes::install_github("dwinsemius/muhaz")

Quick start

library(muhaz)
data(cancer, package = "survival")

# Locally optimal bandwidth (default)
fit <- muhaz(ovarian$futime, ovarian$fustat)
plot(fit)
summary(fit)

# Globally optimal bandwidth
fit_global <- muhaz(ovarian$futime, ovarian$fustat, bw.method = "g")

# Fixed bandwidth
fit_fixed <- muhaz(ovarian$futime, ovarian$fustat, bw.method = "g", bw.grid = 5)

References

Mueller HG, Wang JL. Hazard rates estimation under random censoring with varying kernels and bandwidths. Biometrics 1994; 50: 61–76.
Gefeller O, Dette H. Nearest neighbour kernel estimation of the hazard function from censored data. J Statist Comput Simul 1992; 43: 93–101.
Hess KR, Serachitopol DM, Brown BW. Hazard function estimators: a simulation study. Statistics in Medicine 1999.

License

GPL. See COPYING for details.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.