survDiff: Estimated Difference Between Survival or Cumulative Hazard...

In dynsurv: Dynamic Models for Survival Data

Estimated Difference Between Survival or Cumulative Hazard Functions

Description

survDiff returns estimated survival function or cumulative function from posterior estimates. Note that currently, the function is only applicable to the Bayesian dynamic Cox model with dynamic hazard, where the control argument is specified to be control = list(intercept = TRUE) in function bayesCox.

Usage

survDiff(object, newdata, type = c("survival", "cumhaz"), level = 0.95, ...)

Arguments

object	An object returned by function bayesCox.
newdata	An optional data frame used to generate a design matrix. Note that it must lead to a design matrix with two different design.
type	An optional character value indicating the type of function to compute. The possible values are "survival" and "cumhaz". The former means the estimated survival function; the latter represents the estimated cumulative hazard function for the given newdata.
level	A numerical value between 0 and 1 indicating the level of cradible band.
...	Other arguments for further usage.

Details

The estimated difference between survival curves is a step function representing the difference between the posterior mean survival proportion at the given time grid from the posterior sample. Its credible interval is constructed based on the quantiles of all the pair difference between the survival curves from posterior sample at given credible level.

Value

A data frame with column: "Low", "Mid", "High", "Time", "Design", and "type", and attribute, "surv" valued as "survDiff".

References

Wang, W., Chen, M. H., Chiou, S. H., Lai, H. C., Wang, X., Yan, J., & Zhang, Z. (2016). Onset of persistent pseudomonas aeruginosa infection in children with cystic fibrosis with interval censored data. BMC Medical Research Methodology, 16(1), 122.

See Also

bayesCox, survCurve, and plotSurv.

Examples

## See the examples in bayesCox.

dynsurv documentation built on Aug. 20, 2023, 1:07 a.m.