clusterPIC_Z_DP: Mixed effects PH model for clustered partly interval-censored...
In PICBayes: Bayesian Models for Partly Interval-Censored Data

Description Usage Arguments Details Value Author(s) Examples

Fit a Bayesian semiparametric mixed effects PH model for clustered partly interval-censored data with random effects for one or more predictors. Each random effect follows a DP mixture distribution.

clusterPIC_Z_DP(L, R, y, xcov, IC, scale.designX, scaled, zcov, area, binary, I, order, 
knots, grids, a_eta, b_eta, a_ga, b_ga, a_alpha, b_alpha, H, 
a_tau_star, b_tau_star, beta_iter, phi_iter, beta_cand, phi_cand, 
beta_sig0, x_user, total, burnin, thin, conf.int, seed)

`L`	The vector of left endpoints of the observed time intervals.
`R`	The vector of right endponts of the observed time intervals.
`y`	The vector of censoring indicator: 0=left-censored, 1=interval-censored, 2=right-censored, 3=exact.
`xcov`	The covariate matrix for the p predictors.
`IC`	The vector of general interval-censored indicator: 1=general interval-censored, 0=exact.
`scale.designX`	The TRUE or FALSE indicator of whether or not to scale the design matrix X.
`scaled`	The vector indicating whether each covariate is to be scaled: 1=to be scaled, 0=not.
`zcov`	The design matrix for the q random effects.
`area`	The vector of cluster ID.
`binary`	The vector indicating whether each covariate is binary.
`I`	The number of clusters.
`order`	The degree of basis I-splines: 1=linear, 2=quadratic, 3=cubic, etc.
`knots`	A sequence of knots to define the basis I-splines.
`grids`	A sequence of points at which baseline survival function is to be estimated.
`a_eta`	The shape parameter of Gamma prior for `gamma_l`.
`b_eta`	The rate parameter of Gamma prior for `gamma_l`.
`a_ga`	The shape parameter of Gamma prior for `e^{beta_r}`.
`b_ga`	The rate parameter of Gamma prior for `e^{beta_r}`.
`a_alpha`	The shape parameter of Gamma prior for `alpha`.
`b_alpha`	The rate parameter of Gamma prior for `alpha`.
`H`	The number of distinct components in DP mixture prior under blocked Gibbs sampler.
`a_tau_star`	The shape parameter of `G_0` in DP mixture prior.
`b_tau_star`	The rate parameter of `G_0` in DP mixture prior.
`beta_iter`	The number of initial iterations in the Metropolis-Hastings sampling for `beta_r`.
`phi_iter`	The number of initial iterations in the Metropolis-Hastings sampling for `phi_i`.
`beta_cand`	The sd of the proposal normal distribution in the initial MH sampling for `beta_r`.
`phi_cand`	The sd of the proposal normal distribution in the initial MH sampling for `phi_i`.
`beta_sig0`	The sd of the prior normal distribution for `beta_r`.
`x_user`	The user-specified covariate vector at which to estimate survival function(s).
`total`	The number of total iterations.
`burnin`	The number of burnin.
`thin`	The frequency of thinning.
`conf.int`	The confidence level of the CI for `beta_r`.
`seed`	A user-specified random seed.

The mixed effects PH model is:

h(t_{ij}|x_{ij},z_{i})=h_{0}(t_{ij})exp(beta'x_{ij}+phi_{i}'z_{i}),

for the jth subject in the ith cluster.

Each of the q random effects is sampled using MH algorithm separately.

a list containing the following elements:

`N`	The sample size.
`parbeta`	A `total` by `p` matrix of MCMC draws of `beta_r`, r=1, ..., p.
`parsurv0`	A `total` by `length(grids)` matrix, each row contains the baseline survival at `grids` from one iteration.
`parsurv`	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.
`paralpha`	A `total` by `q` vector of MCMC draws of `alpha`.
`coef`	A vector of regression coefficient estimates.
`coef_ssd`	A vector of sample standard deviations of regression coefficient estimates.
`coef_ci`	The credible intervals for the regression coefficients.
`S0_m`	The estimated baseline survival at `grids`.
`S_m`	The estimated survival at `grids` with user-specified covariate values `x_user`.
`grids`	The sequance of points where baseline survival function is estimated.
`DIC`	Deviance information criterion.
`NLLK`	Negative log pseudo-marginal likelihood.

Chun Pan

# Number of iterations set to very small for CRAN automatic testing
data(da4)
J=rep(1,nrow(da4))
zcov=cbind(J,da4[,4])
try8<-PICBayes(formula=Surv(L,R,type='interval2')~x1+x2,data=data.frame(da4),
model='clusterPIC_Z_DP',IC=da4[,7],scale.designX=TRUE,scaled=c(1,0),zcov=zcov,
area=da4[,6],binary=c(0,1),I=25,order=3,knots=c(0,2,6,max(da4[,1:2],na.rm=TRUE)+1),
grids=seq(0.1,10.1,by=0.1),a_eta=1,b_eta=1,a_ga=1,b_ga=1,a_alpha=1,b_alpha=1,H=5,
a_tau_star=1,b_tau_star=1,beta_iter=11,phi_iter=11,beta_cand=1,phi_cand=1,
beta_sig0=10,x_user=NULL,total=20,burnin=10,thin=1,conf.int=0.95,seed=1)