survBayes {survBayes}R Documentation

Fits a proportional hazards model to time to event data by a Bayesian approach

Description

Fits a proportional hazards model to time to event data by a Bayesian approach. Right and interval censored data and a lognormal frailty term can be fitted.

Usage

survBayes(formula = formula(data), data = parent.frame(), burn.in = 1000, number.sample = 1000, max.grid.size = 20, control, control.frailty, seed.set = 100, ...)

Arguments

formula a formula object, with the response on the left of a ~ operator, and the terms on the right. The response must be a survival object of type "right" or "interval" as returned by the Surv function.
data a data.frame in which to interpret the variables named in the formula
burn.in burn.in
number.sample number of sample
max.grid.size number of grid points
control Object of class control specifying iteration limit and other control options. Default is survBayes.control(...).
control.frailty Object of class control.frailty specifying parameters for the priors of frailties and other control options. Default is survBayes.control.lognormal.frailty(...) or survBayes.control.gamma.frailty(...).
seed.set setting of the seed of the random number generator
... further parameters

Details

Fits a proportional hazards model to time to event data by a Bayesian approach. The time axis is split into max.grid.size intervals and the log baseline hazard is assumed to be cubic spline penalized by an auto regressive process of order one. Right and interval censored data and a lognormal or gamma frailty term can be fitted. In case of interval censored data the assumed observation times are augmented by a piecewise exponential distribution conditioned on the respective interval.

Value

The returned values are, if appropriate

t.where used grid points
beta samples of the vector of covariates
lbh.coef samples of the log baseline hazard coefficients at the grid points
sigma.lbh samples of sigma.lbh.0 and sigma.lbh.1
alpha.cluster samples of the frailty values
sigma.cluster samples of frailty variance
z.cluster samples of the frailty values
mu.cluster samples of the rate and shape of the gamma prior
m.h.performance The performance of the Metropolis-Hasting steps is checked for beta, lbh and, if appropriate, alpha.cluster or mu.cluster

Author(s)

V. Henschel, Ch. Heiss, U. Mansmann

See Also

coxph, Surv

Examples

data(aneurism.data)
control<-survBayes.control(sigma.lbh.1=0.01,rate.sigma.lbh.1 = 1e-3, shape.sigma.lbh.1 = 1e-3)
aneurism.res<-survBayes(Surv(left,right,cens*3,type="interval")~mo+loc+frailty(gr,dist="gamma"),data=aneurism.data,burn.in=0,number.sample=10,control=control)

[Package survBayes version 0.2.1.1 Index]