CCuMRes {BGPhazard}  R Documentation 
Bayesian Semiparametric Cure Rate Model with an Unknown Threshold and Covariate Information
Description
Posterior inference for the bayesian semiparmetric cure rate model with covariates in survival analysis.
Usage
CCuMRes(
data,
covs.x = names(data)[seq.int(3, ncol(data))],
covs.y = names(data)[seq.int(3, ncol(data))],
type.t = 3,
K = 50,
utao = NULL,
alpha = rep(0.01, K),
beta = rep(0.01, K),
c.r = rep(0, K  1),
c.nu = 1,
var.theta.str = 25,
var.delta.str = 25,
var.theta.ini = 100,
var.delta.ini = 100,
type.c = 4,
a.eps = 0.1,
b.eps = 0.1,
epsilon = 1,
iterations = 5000,
burn.in = floor(iterations * 0.2),
thinning = 3,
printtime = TRUE
)
Arguments
data 
Double tibble. Contains failure times in the first column, status indicator in the second, and, from the third to the last column, the covariate(s). 
covs.x 
Character. Names of covariables to be part of the multiplicative part of the hazard 
covs.y 
Character. Names of covariables to determine the cure threshold por each patient. 
type.t 
Integer. 1=computes uniformlydense intervals; 2= partition arbitrarily defined by the user with parameter utao and 3=same length intervals. 
K 
Integer. Partition length for the hazard function. 
utao 
vector. Partition specified by the user when type.t = 2. The first value of the vector has to be 0 and the last one the maximum observed time, either censored or uncensored. 
alpha 
Nonnegative entry vector. Small entries are recommended in order to specify a noninformative prior distribution. 
beta 
Nonnegative entry vector. Small entries are recommended in order to specify a noninformative prior distribution. 
c.r 
Nonnegative vector. The higher the entries, the higher the correlation of two consective intervals. 
c.nu 
Tuning parameter for the proposal distribution for c.
Only when 
var.theta.str 
Double. Variance of the proposal normal distribution for theta in the MetropolisHastings step. 
var.delta.str 
Double. Variance of the proposal normal distribution for delta in the MetropolisHastings step. 
var.theta.ini 
Double. Variance of the prior normal distribution for theta. 
var.delta.ini 
Double. Variance of the prior normal distribution for delta. from the acceptance ratio in the MetropolisHastings algorithm for delta*. 
type.c 
1=defines 
a.eps 
Double. Shape parameter for the prior gamma distribution of
epsilon when 
b.eps 
Double. Scale parameter for the prior gamma distribution of
epsilon when 
epsilon 
Double. Mean of the exponencial distribution assigned to

iterations 
Integer. Number of iterations including the 
burn.in 
Integer. Length of the burnin period for the Markov chain. 
thinning 
Integer. Factor by which the chain will be thinned. Thinning the Markov chain reduces autocorrelation. 
printtime 
Logical. If 
Details
Computes the Gibbs sampler with the full conditional distributions of
all model parameters (NietoBarajas & Yin, 2008) and arranges the resulting Markov
chain into a tibble which can be used to obtain posterior summaries. Prior
distributions for the regression coefficients Theta and Delta are assumend
independent normals with zero mean and variance var.theta.ini
,
var.delta.ini
, respectively.
Note
It is recommended to verify chain's stationarity. This can be done by
checking each element individually. See CCuPlotDiag
.
References
 NietoBarajas, L. E., & Yin, G. (2008). Bayesian semiparametric cure rate model with an unknown threshold. Scandinavian Journal of Statistics, 35(3), 540556. https://doi.org/10.1111/j.14679469.2007.00589.x
 Nietobarajas, L. E. (2002). Discrete time Markov gamma processes and time dependent covariates in survival analysis. Statistics, 25.
See Also
Examples
# data(BMTKleinbook)
# res < CCuMRes(BMTKleinbook, covs.x = c("tTransplant","hodgkin","karnofsky","waiting"),
# covs.y = c("tTransplant","hodgkin","karnofsky","waiting"),
# type.t = 2, K = 72, length = 30,
# alpha = rep(2,72), beta = rep(2,72), c.r = rep(50, 71), type.c = 2,
# var.delta.str = .1, var.theta.str = 1,
# var.delta.ini = 100, var.theta.ini = 100,
# iterations = 100, burn.in = 10, thinning = 1)