Fit proportional hazards model for case 1 interval-censored data. Use MCMC method to estimate regression coefficients, baseline survival,
and survival at user-specified covariate values.
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: 1=left-censored, 0=right-censored.
xcov
a matrix of covariates, each column corresponds to one covariate.
x_user
a user specified vector of covariate values
order
degree of I-splines (b_l) (see details). Recommended values are 2-4.
sig0
standard deviation of normal prior for each regression coefficient beta_r.
coef_range
specify support domain of target density for beta_r sampled by arms (see details).
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.
niter
total number of iterations of MCMC chains.
Details
The baseline cumulative hazard is approximated by a linear combination of I-splines:
sum_{l=1}^{k}(gamma_l*b_l).
Function arms is used to sample each regression coefficient beta_r, and coef_range specifies the support of
the indFunc
in arms.
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.
Author(s)
Bo Cai
References
Cai, B., Lin, X., and Wang, L. (2011). Bayesian proportional hazards model for current status data with monotone splines.
Computational Statistics and Data Analysis, 55 2644-2651.
providing 
.