psbcGroup (version 1.7)

psbcEN: Function to Fit the Penalized Semiparametric Bayesian Cox Model with Elastic Net Prior

Description

Penalized semiparametric Bayesian Cox (PSBC) model with elastic net prior is implemented to analyze survival data with high-dimensional covariates.

Usage

psbcEN(survObj, priorPara, initial, rw=FALSE, mcmcPara, num.reps, 
		thin, chain = 1, save = 1000)

Value

psbcEN returns an object of class psbcEN

beta.p

posterior samples for \(\beta\)

h.p

posterior samples for \(h\)

tauSq.p

posterior samples for \(\tau^2\)

mcmcOutcome

The list containing posterior samples for the remaining model parameters

Arguments

survObj

The list containing observed data from n subjects; t, di, x

priorPara

The list containing prior parameter values; eta0, kappa0, c0, r1, r2, delta1, delta2, s

initial

The list containing the starting values of the parameters; beta.ini, lambda1Sq, lambda2, sigmaSq, tauSq, h

rw

When setting to "TRUE", the conventional random walk Metropolis Hastings algorithm is used. Otherwise, the mean and the variance of the proposal density is updated using the jumping rule described in Lee et al. (2011).

mcmcPara

The list containing the values of options for Metropolis-Hastings step for \(\beta\); numBeta, beta.prop.var

num.reps

the number of iterations of the chain

thin

thinning

chain

the numeric name of chain in the case when running multiple chains.

save

frequency of storing the results in .Rdata file. For example, by setting "save = 1000", the algorithm saves the results every 1000 iterations.

Author

Kyu Ha Lee, Sounak Chakraborty, (Tony) Jianguo Sun

Details

t	a vector of n times to the event
di	a vector of n censoring indicators for the event time (1=event occurred, 0=censored)
x	covariate matrix, n observations by p variables
eta0	scale parameter of gamma process prior for the cumulative baseline hazard, \(eta0 > 0\)
kappa0	shape parameter of gamma process prior for the cumulative baseline hazard, \(kappa0 > 0\)
c0	the confidence parameter of gamma process prior for the cumulative baseline hazard, \(c0 > 0\)
r1	the shape parameter of the gamma prior for \(\lambda_1^2\)
r2	the shape parameter of the gamma prior for \(\lambda_2\)
delta1	the rate parameter of the gamma prior for \(\lambda_1^2\)
delta2	the rate parameter of the gamma prior for \(\lambda_2\)
s	the set of time partitions for specification of the cumulative baseline hazard function
beta.ini	the starting values for \(\beta\)
lambda1Sq	the starting value for \(\lambda_1^2\)
lambda2	the starting value for \(\lambda_2\)
sigmaSq	the starting value for \(\sigma^2\)
tauSq	the starting values for \(\tau^2\)
h	the starting values for \(h\)
numBeta	the number of components in \(\beta\) to be updated at one iteration
beta.prop.var	the variance of the proposal density for \(\beta\) when rw is set to "TRUE"

References

Lee, K. H., Chakraborty, S., and Sun, J. (2011). Bayesian Variable Selection in Semiparametric Proportional Hazards Model for High Dimensional Survival Data. The International Journal of Biostatistics, Volume 7, Issue 1, Pages 1-32.

Lee, K. H., Chakraborty, S., and Sun, J. (2015). Survival Prediction and Variable Selection with Simultaneous Shrinkage and Grouping Priors. Statistical Analysis and Data Mining, Volume 8, Issue 2, pages 114-127.

Examples

if (FALSE) {

# generate some survival data
	
	set.seed(204542)
	
	p = 20
	n = 100
	beta.true <- c(rep(4, 10), rep(0, (p-10)))	
	
	CovX<- diag(0.1, p)
	
	survObj 	<- list()
	survObj$x	<- apply(rmvnorm(n, sigma=CovX, method="chol"), 2, scale)
	
	pred <- as.vector(exp(rowSums(scale(survObj$x, center = FALSE, scale = 1/beta.true))))
	
	t 		<- rexp(n, rate = pred)
	cen		<- runif(n, 0, 8)      
	survObj$t 		<- pmin(t, cen)
	survObj$di 		<- as.numeric(t <= cen)

	priorPara 			<- list()
	priorPara$eta0 		<- 1
	priorPara$kappa0 	<- 1
	priorPara$c0 		<- 2
	priorPara$r1		<- 0.1
	priorPara$r2		<- 1
	priorPara$delta1	<- 0.1
	priorPara$delta2	<- 1
	priorPara$s			<- sort(survObj$t[survObj$di == 1])
	priorPara$s			<- c(priorPara$s, 2*max(survObj$t)
	- max(survObj$t[-which(survObj$t==max(survObj$t))]))
	priorPara$J			<- length(priorPara$s)

	mcmcPara				<- list()
	mcmcPara$numBeta		<- p
	mcmcPara$beta.prop.var	<- 1

	initial				<- list()
	initial$beta.ini	<- rep(0.5, p)
	initial$lambda1Sq	<- 1  
	initial$lambda2		<- 1  
	initial$sigmaSq		<- runif(1, 0.1, 10)
	initial$tauSq		<- rexp(p, rate = initial$lambda1Sq/2)
	initial$h			<- rgamma(priorPara$J, 1, 1)

	rw = FALSE
	num.reps = 20000
	chain = 1
	thin = 5
	save = 5

	fitEN <- psbcEN(survObj, priorPara, initial, rw=FALSE, mcmcPara, 
				num.reps, thin, chain, save)

	vs <- VS(fitEN, X=survObj$x)
    
	}