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.
case1ph(L, R, status, xcov, x_user, order, sig0, coef_range,
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: 1=left-censored, 0=right-censored.
a matrix of covariates, each column corresponds to one covariate.
a user specified vector of covariate values
degree of I-splines (b_l) (see details). Recommended values are 2-4.
standard deviation of normal prior for each regression coefficient beta_r.
specify support domain of target density for beta_r sampled by arms (see details).
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.
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 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.
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.