Fit a Bayesian semiparametric PH model with random intercept for
clustered general interval-censored data.
Random intercept follows a normal distribution N(0, tau^{-1}).
clusterIC_int(L, R, y, xcov, IC, scale.designX, scaled, area, binary, I,
order, knots, grids, a_eta, b_eta, a_ga, b_ga, a_tau, b_tau, beta_iter, phi_iter,
beta_cand, phi_cand, beta_sig0, x_user, total, burnin, thin, conf.int, seed)The vector of left endpoints of the observed time intervals.
The vector of right endponts of the observed time intervals.
The vector of censoring indicator: 0=left-censored, 1=interval-censored, 2=right-censored.
The covariate matrix for the p predictors.
The vector of general interval-censored indicator: 1=general interval-censored, 0=exact.
The TRUE or FALSE indicator of whether or not to scale the design matrix X.
The vector indicating whether each covariate is to be scaled: 1=to be scaled, 0=not.
The vector of cluster ID.
The vector indicating whether each covariate is binary.
The number of clusters.
The degree of basis I-splines: 1=linear, 2=quadratic, 3=cubic, etc.
A sequence of knots to define the basis I-splines.
A sequence of points at which baseline survival function is to be estimated.
The shape parameter of Gamma prior for gamma_l.
The rate parameter of Gamma prior for gamma_l.
The shape parameter of Gamma prior for e^{beta_r}.
The rate parameter of Gamma prior for e^{beta_r}.
The shape parameter of Gamma prior for random intercept precision tau.
The rate parameter of Gamma prior for random intercept precision tau.
The number of initial iterations in the Metropolis-Hastings sampling for beta_r.
The number of initial iterations in the Metropolis-Hastings sampling for phi_i.
The sd of the proposal normal distribution in the initial MH sampling for beta_r.
The sd of the proposal normal distribution in the initial MH sampling for phi_i.
The sd of the prior normal distribution for beta_r.
The user-specified covariate vector at which to estimate survival function(s).
The number of total iterations.
The number of burnin.
The frequency of thinning.
The confidence level of the CI for beta_r.
A user-specified random seed.
a list containing the following elements:
The sample size.
A total by p matrix of MCMC draws of beta_r, r=1, ..., p.
A total by length(grids) matrix, each row contains the baseline survival at grids from one iteration.
A total 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 total by I matrix of MCMC draws of phi_i, i=1,...,I.
A total by 1 vector of MCMC draws of tau.
A vector of regression coefficient estimates.
A vector of sample standard deviations of regression coefficient estimates.
The credible intervals for the regression coefficients.
The estimated baseline survival at grids.
The estimated survival at grids with user-specified covariate values x_user.
The sequance of points where baseline survival function is estimated.
Deviance information criterion.
Negative log pseudo-marginal likelihood.
The baseline cumulative hazard is approximated by a linear combination of I-splines:
sum_{l=1}^{K}(gamma_l*b_l(t)).
For a binary prdictor, we sample e^{beta_r}, with Gamma prior.
The regression coefficient beta_r for a continuous predictor and random intercept phi_i are sampled using MH algorithm.
During the initial beta_iter iterations, sd of the proposal distribution is beta_cand.
Afterwards, proposal sd is set to be the sd of available MCMC draws.
Same method for phi_i.