GaPloth {BGPhazard}

Plots for the Hazard and Survival Function Estimates

Description

Plots the hazard function and with the survival function estimates defined by the Markov gamma process with and without covariates (Nieto-Barajas & Walker, 2002).

Usage

GaPloth(
  M,
  type.h = "segment",
  addSurvival = T,
  intervals = T,
  confidence = 0.95,
  summary = FALSE
)

Arguments

M

tibble. Contains the output by CGaMRres and GaMRes.

type.h

character. "segment"= use segments to plot hazard rates, "line" = link hazard rates by a line

addSurvival

Logical. If TRUE, Nelson-Aalen estimate is plotted over the hazard function and Kaplan-Meier estimate is plotted over the survival function.

intervals

logical. If TRUE, plots confidence bands for the selected functions including Nelson-Aalen and/or Kaplan-Meier estimate.

confidence

Numeric. Confidence level.

summary

Logical. If TRUE, a summary for hazard and survival functions is returned as a tibble.

Details

This function returns estimators plots for the resulting hazard rate as it is computed by GaMRes and CGaMRes and the Nelson-Aalen estimate along with their confidence intervals for the data set given. Additionally, it plots the survival function and the Kaplan-Meier estimate with their corresponding credible/confidence 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 a grid 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. & Walker, S. G. (2002). Markov beta and gamma processes for modelling hazard rates. Scandinavian Journal of Statistics 29: 413-424.

See Also

GaMRes, CGaMRes, CGaPlotDiag, GaPlotDiag

Examples

## Simulations may be time intensive. Be patient.

## Example 1
#  data(gehan)
#  timesG <- gehan$time[gehan$treat == "6-MP"]
#  deltaG <- gehan$cens[gehan$treat == "6-MP"]
#  GEX1 <- GaMRes(timesG, deltaG, K = 8, iterations = 3000)
#  GaPloth(GEX1)


## Example 2
#  data(leukemiaFZ)
#  timesFZ <- leukemiaFZ$time
#  deltaFZ <- leukemiaFZ$delta
#  GEX2 <- GaMRes(timesFZ, deltaFZ, type.c = 4)
#  GaPloth(GEX2)