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.
case2probit(L, R, status, xcov, x_user, order, m0,
v0, a_eta, b_eta, knots, grids, niter, seed)a numeric vector of left timepoints of observed time intervals.
a numeric vector of right timepoints of observed time intervals.
a vector of censoring indicators: 0=left-censored, 1=interval-censored, 2=right-censored.
a matrix of covariates, each column corresponds to one covariate.
a vector of user specified covariate values.
degree of I-splines (b_l) (see details). Recommended values are 2-4.
mean of normal prior for gamma_0.
precision of normal prior for gamma_0.
shape parameter of Gamma prior for gamma_l (see details).
rate parameter of Gamma prior for gamma_l (see details).
a sequence of points to define I-splines.
a sequence of points where baseline survival function is to be estimated. Default is minimum observed time points.
total number of iterations of MCMC chains.
a user specified random seed, default is NULL.
a list containing the following elements:
a niter by p matrix of MCMC draws of beta_r, r=1, ..., p.
a niter by length(grids) matrix, each row contains the baseline survival at grids from one iteration.
a niter by length(grids)*G matrix, each row contains the survival at grids from one iteration.
G is the number of sets of user-specified covariate values.
a niter by n matrix, each row contains the inverse PDF of observed interval-censored data from one iteration.
This is used for computing LPML later.
a sequence of points where baseline survival is estimated.
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.
Lin, X. and Wang, L. (2009). A semiparametric probit model for case 2 interval-censored failure time data. Statistics in Medicine 29 972-981.