CCuPloth: Plots for the Hazard and Survival Funcion Estimates

In BGPhazard: Markov Beta and Gamma Processes for Modeling Hazard Rates

Plots for the Hazard and Survival Funcion Estimates

Description

Plots the resulting hazard function and the survival function estimates defined by the bayesian semiparametric cure rate model with an unknown threshold (Nieto-Barajas & Yin, 2008).

Usage

CCuPloth(
  M,
  new_obs = NULL,
  type.h = "segment",
  qn = 0.5,
  intervals = T,
  confidence = 0.95,
  summary = FALSE
)

Arguments

M	tibble. Contains the output generated by CuMRres.
new_obs	tibble. Contains the covariate information for new observations.
type.h	character. "segment"= use segments to plot hazard rates, "line" = link hazard rates by a line
qn	Numeric. Quantile for Tao (cure time) that should be visualized on the plot.
intervals	logical. If TRUE, plots credible intervals.
confidence	Numeric. Confidence level.
summary	Logical. If TRUE, a summary for the hazard and survival functions is returned as a tibble.

Details

This function returns estimators plots for the hazard rate as it is computed by CCuMRes and the cure time (quantile of Tao specified by the user) together with credible intervals. Additionally, it plots the survival function and the cure proportion estimates with their corresponding credible intervals.

Value

SUM.h	Numeric tibble. Summary for the mean, median, and a confint / 100 confidence interval for each segment of the hazard function. If summary = TRUE
SUM.S	Numeric tibble. Summary for the mean, median, and a confint / 100 confidence interval for each segment of the survival function. If summary = TRUE

References

- Nieto-Barajas, L. E. (2003). Discrete time Markov gamma processes and time dependent covariates in survival analysis. Bulletin of the International Statistical Institute 54th Session. Berlin. (CD-ROM).

-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

See Also

CCuMRes,

Examples

## Simulations may be time intensive. Be patient.

## Example 1
# 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)
    # 
    # CCuPloth(res, type.h = "segment",qn=.5, summary = T)
    # 
    # new_obs <- tibble(tTransplant=c(0,0,0,0),
    #                       hodgkin=c(0,1,0,1),
    #                       karnofsky=c(90,90,60,60),
    #                       waiting=c(36,36,36,36)
    # )
    # 
    # ind <- CCuPloth(res, new_obs, qn = .5)
    # ind

BGPhazard documentation built on Sept. 3, 2023, 5:09 p.m.