icensBKL (version 1.5)

NPbayesSurv: Bayesian non-parametric estimation of a survival curve with right-censored data

Description

Bayesian non-parametric estimation of a survival curve for right-censored data as proposed by Susarla and Van Ryzin (1976, 1978)

Usage

NPbayesSurv(time, censor,
  choice = c("exp", "weibull", "lnorm"), c = 1, parm,
  xlab = "Time", ylab = "Survival Probability", maintitle = "",
  cex.lab = 1.2, cex.axis = 1.0, cex.main = 1.5, cex.text = 1.2, lwd = 2)

Value

A vector corresponding to the parm argument

Arguments

time, censor

numeric vectors with (right-censored) survival times and 0/1 censoring indicators (1 for event, 0 for censored)

choice

a character string indicating the initial guess (\(S^*\)) of the survival distribution

c

parameter of the Dirichlet process prior

parm

a numeric vector of parameters for the initial guess: rate parameter for exponential (see also Exponential), a two-element vector with shape and scale parameters for weibull (see also Weibull), a two-element vector with meanlog and sdlog parameters for log-normal (see also Lognormal). If not given, parameters for the initial guess are taken from the ML fit

xlab, ylab

labels for axes of the plot

maintitle

text for the main title

cex.lab, cex.axis, cex.main, cex.text, lwd

graphical parameters

Author

Emmanuel Lesaffre , Arnošt Komárek

References

Susarla, V. and Van Ryzin, J. (1976). Nonparametric Bayesian estimation of survival curves from incomplete observations. Journal of the American Statistical Association, 71(356), 897-902.

Susarla, V. and Van Ryzin, J. (1978). Large sample theory for a Bayesian nonparametric survival curve estimator based on censored samples. The Annals of Statistics, 6(4), 755-768.

Examples

## Nonparametric Bayesian estimation of a survival curve
## Homograft study, aortic homograft patients
data("graft", package = "icensBKL")

graft.AH <- subset(graft, Hgraft == "AH") # aortic homograft patients
time <- graft$timeFU[graft$Hgraft == "AH"]
censor <- graft$homo.failure[graft$Hgraft == "AH"]

  ## Initial guess: Weibull, c = 0.1 and 100
oldpar <- par(mfrow = c(1, 2))
NPbayesSurv(time, censor, "weibull", c = 100,
   xlab = "Follow-up time since the operation (years)", maintitle = "c = 100")
NPbayesSurv(time, censor, "weibull", c = 100,
   xlab = "Follow-up time since the operation (years)", maintitle = "c = 100")
par(oldpar)

  ## Initial guess: Exponential, c = 100
oldpar <- par(mfrow = c(1, 1))
NPbayesSurv(time, censor, "exp", c = 100,
   xlab = "Follow-up time since the operation (years)", maintitle = "Exp: c = 100")

  ## Initial guess: Log-normal, c = 100
NPbayesSurv(time, censor, "lnorm", c = 100,
   xlab = "Follow-up time since the operation (years)", maintitle = "Log-Normal: c = 100")
par(oldpar)