CCuMRes: 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, length,
  K = 50, 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 uniformly-dense intervals; 2=length intervals defined by the user and 3=same length intervals.

length

Integer. Interval length of the partition.

Integer. Partition length for the hazard function.

alpha

Nonnegative entry vector. Small entries are recommended in order to specify a non-informative prior distribution.

beta

Nonnegative entry vector. Small entries are recommended in order to specify a non-informative 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 type.c is 3 or 4.

var.theta.str

Double. Variance of the proposal normal distribution for theta in the Metropolis-Hastings step.

var.delta.str

Double. Variance of the proposal normal distribution for delta in the Metropolis-Hastings 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 Metropolis-Hastings algorithm for delta*.

type.c

1=defines c.r as a zero-entry vector; 2=lets the user define c.r freely; 3=assigns c.r an exponential prior distribution with mean 1; 4=assigns c.r an exponential hierarchical distribution with mean epsilon which in turn has a Ga(a.eps, b.eps) distribution.

a.eps

Double. Shape parameter for the prior gamma distribution of epsilon when type.c = 4.

b.eps

Double. Scale parameter for the prior gamma distribution of epsilon when type.c = 4.

epsilon

Double. Mean of the exponencial distribution assigned to c.r when type.c = 3.

iterations

Integer. Number of iterations including the burn.in to be computed for the Markov chain.

burn.in

Integer. Length of the burn-in period for the Markov chain.

thinning

Integer. Factor by which the chain will be thinned. Thinning the Markov chain reduces autocorrelation.

printtime

Logical. If TRUE, prints out the execution time.

Details

Computes the Gibbs sampler with the full conditional distributions of all model parameters (Nieto-Barajas & 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.

References

- Nieto-Barajas, L. E., & Yin, G. (2008). Bayesian semiparametric cure rate model with an unknown threshold. Scandinavian Journal of Statistics, 35(3), 540-556. https://doi.org/10.1111/j.1467-9469.2007.00589.x

- Nieto-barajas, L. E. (2002). Discrete time Markov gamma processes and time dependent covariates in survival analysis. Statistics, 2-5.

Examples

Run this code

# NOT RUN {


# 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)



# }