SCRSELECTRETURN: Performs Bayesian Variable Selection on the covariates in a...

In AndrewGChapple/SCRSELECT: Performs Bayesian Variable Selection on the Covariates in a Semi-Competing Risks Model

Description

Performs Bayesian Variable Selection on the covariates in a semi-competing risks model and returns burned in posterior means of parameters. This function is used in the ReturnModel function.

Usage

SCRSELECTRETURN(Y1, I1, Y2, I2, X, hyperparameters, beta1start, beta2start,
  beta3start, B, inc, burn)

Arguments

Y1	Vector Containing non-terminal event times (or censoring time due to death/censoring)
I1	Vector Containing non-terminal event indicators (1 if non-terminal event for a patient, 0 otherwise)
Y2	Vector Containing Terminal Event times (or censoring)
I2	Vector Containing Terminal event indicators (1 if a patients experiences a non-ternminal event, 0 if censored)
X	Matrix of Patient Covariates. The last inc will be left out of variable selection.
hyperparameters	List containing 29 hyperparameters and four starting values. In order they are: psi-the swap rate of the SVSS algorithm. c-parameter involved in Sigma matrix for selection. z1a, z1b, z2a, z2b, z3a, z3b - beta hyper parameters on probability of inclusion for each of the three hazard functions. a1,b1,a2,b2,a3,b3- hyperparameters on sigma_lambda_1, sigma_lambda_2, and sigma_lambda_3. clam1, clam2, clam3 - spatial dependency of baseline hazard (between 0 and 1) for the three hazard functions. Alpha1, Alpha2, Alpha3 - The parameter for the number of split points in hazards 1,2 and 3 (must be whole number). J1max, J2max, J3max - Maximum number of split points allowed (must be whole number). J1, J2, J3- Starting number of split points. w, psi1- hyperparameters on theta^-1. cep=Tuning Parameter for theta^-1 sampler. epstart-Starting value for theta^-1. cl1,cl2,cl3-Tuning parameters for log baseline hazard height sampler.
beta1start	Starting Values for Beta1
beta2start	Starting Values for Beta2
beta3start	Starting Values for Beta3
B	Number of iterations
inc	Number of variables left out of selection
burn	percent of posterior sample to burn in (burn*B must be a whole number)

Value

Returns a list the following posterior quantities after burn in: Marginal probability of inclusion, mean frailty parameters, and the baseline hazard samples for each hazard.

References

[1] Lee, K. H., Haneuse, S., Schrag, D. and Dominici, F. (2015), Bayesian semiparametric analysis of semicompeting risks data: investigating hospital readmission after a pancreatic cancer diagnosis. Journal of the Royal Statistical Society: Series C (Applied Statistics), 64: 253-273. doi: 10.1111/rssc.12078

Examples

####Randomly Generate Semicompeting Risks Data
set.seed(1)
####Generates random patient time, indicator and covariates.
n=100
Y1=runif(n,0,100)
I1=rbinom(n,1,.5)
Y2=Y1
I2=I1
for(i in 1:n){if(I1[i]==0){Y2[i]=Y1[i]}else{Y2[i]=Y1[i]+runif(1,0,100)}}
I2=rbinom(n,1,.5)
library(mvtnorm)
X=rmvnorm(n,rep(0,7),diag(7))
####Read in Hyperparameters
##Swap Rate
psi=.5
c=5
###Eta Beta function probabilities
z1a=.4
z1b=1.6
z2a=.4
z2b=1.6
z3a=.4
z3b=1.6
####Hierarchical lam params
###Sigma^2 lambda_g hyperparameters
a1=.7
b1=.7
a2=a1
b2=b1
a3=a1
b3=b1
##Spacing dependence c in [0,1]
clam1=1
clam2=1
clam3=1
#####NumSplit
alpha1=3
alpha2=3
alpha3=3
J1max=10
J2max=10
J3max=10
####Split Point Starting Value ###
J1=3
J2=3
J3=3
###epsilon starting values/hyperparameters###
w=.7
psi1=.7
cep=2.4
#############
epstart=1.5
cl1=.25
cl2=.25
cl3=.25
###Beta Starting Values
beta1start=c(1,1,1,1,1,-1,-1)
beta2start=c(1,1,1,1,1,-1,-1)
beta3start=c(-1,1,1,1,1,-1,-1)
hyper1=c(psi,c,z1a,z1b,z2a,z2b,z3a,z3b,a1,b1,a2,b2,a3,b3,clam1,clam2,clam3)
hyper2=c(alpha1,alpha2,alpha3,J1max,J2max,J3max,J1,J2,J3,w,psi1,cep,epstart,cl1,cl2,cl3)
hyper=c(hyper1,hyper2)
###Number of iterations and output location
B=100
Path=tempdir()
###Number of variables to exclude from selection and burnin percent
inc=2
burn=.1
SCRSELECTRETURN(Y1,I1,Y2,I2,X,hyper,beta1start,beta2start,beta3start,B,inc,burn)

AndrewGChapple/SCRSELECT documentation built on May 28, 2019, 11:04 a.m.