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ICBayes (version 1.0)

case2ph: PH model for general interval-censored data

Description

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.

Usage

case2ph(L, R, status, xcov, x_user, order, sig0, coef_range, 
	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: 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.

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 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.

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.