Fit proportional hazards model for general interval-censored data. Use MCMC method to estimate regression coefficients, baseline survival,
and survival function 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 modeled 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
Lin, X., Cai, B., Wang, L., and Zhang, Z. (2015). Bayesian proportional hazards model for general interval-censored data.
Lifetime Data Analysis, 21 470-490.
providing 
.