case1po: PO model for case 1 interval-censored data

In ICBayes: Bayesian Semiparametric Models for Interval-Censored Data

Description

Fit proportional odds model for case 1 interval-censored data. Use MCMC method to estiamte regression coefficients, baseline survival, and survival function at user-specified covariate values.

Usage

case1po(L, R, status, xcov, x_user, order, sig0, coef_range, 
	a_eta, b_eta, knots, grids, niter, seed)

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 vector of user specified 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.
seed	a user specified random seed, default is NULL.

Details

The baseline odds function 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.
parsurv	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.
parfinv	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.

Author(s)

Xiaoyan Lin

References

Lin, X. and Wang, L. (2011). Bayesian proportional odds model for analyzing current status data: univariate, clustered, and multivariate. Communication in Statistics-Simulation and Computation, 40 1171-1181.

ICBayes documentation built on Feb. 1, 2020, 1:07 a.m.