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ICBayes (version 1.0)

case2probit: Probit model for general interval-censored data

Description

Fit probit model to general interval-censored data. Use MCMC method to estimate regression coefficients, baseline survival, and survival function at user-specified covariate values.

Usage

case2probit(L, R, status, xcov, x_user, order,
	    v0, a_eta, b_eta, knots, grids, niter)

Arguments

L
a numeric vector of left timepoints of observed time intervals.
R
a numeric vector of right timepoints of observed time intervals.
status
a vector of censoring indicators: 0=left-censored, 1=interval-censored, 2=right-censored.
xcov
a matrix of covariates, each column corresponds to one covariate.
x_user
a vector of user specified covariate values.
order
degree of I-splines (b_l) (see details). Recommended values are 2-4.
v0
precision of normal prior for gamma_0.
a_eta
shape parameter of Gamma prior for gamma_l (see details).
b_eta
rate parameter of Gamma prior for gamma_l (see details).
knots
a sequence of points to define I-splines.
grids
a sequence of points where baseline survival function is to be estimated. Default is minimum observed time points.
niter
total number of iterations of MCMC chains.

Value

a list containing the following elements:
parbeta
a niter by p matrix of MCMC draws of beta_r, r=1, ..., p.
parsurv0
a niter by length(grids) matrix, each row contains the baseline survival at grids from one iteration .
grids
a sequence of points where baseline survival is estimated.

Details

The baseline function is modeled by a linear combination of I-splines:

gamma_0+sum_{l=1}^{k}(gamma_l*b_l).

Regression coefficient vector beta is sampled from a multivariate normal distribution. For more information, please see reference.

References

Lin, X. and Wang, L. (2009). A semiparametric probit model for case 2 interval-censored failure time data. Statistics in Medicine 29 972-981.