weibullDiag: Diagnostic plot of adequacy of Weibull distribution

In felix-hof/biostatUZH: Misc Tools of the Department of Biostatistics, EBPI, University of Zurich

Diagnostic plot of adequacy of Weibull distribution

Description

This function constructs a diagnostic plot of the adequacy of the Weibull distribution for survival data with respect to one categorical covariate. If the Weibull distribution fits the data well, then the lines produced should be linear and parallel.

Usage

weibullDiag(formula, data, labels = NULL)

Arguments

formula	A formula containing a Surv object, should only contain one categorical predictor, or a set of indicators describing only one predictor.
data	Data set.
labels	A vector containing labels for the plotted lines.

Details

As discussed in Klein and Moeschberger (2003), one method for checking the adequacy of the Weibull model with a categorical covariate is to produce stratified Kaplan-Meier estimates (KM), which can be transformed to estimate the log cumulative hazard for each stratum. Then in a plot of \log(t) versus \log(-\log(KM)), the lines should be linear and parallel. This can be seen as the log cumulative hazard for the Weibull distribution is

\log H(t) = \log \lambda + \alpha \log t.

Value

Produces a plot of log Time vs. log Estimated Cumulative Hazard for each level of the predictor (similarly to what can be obtained using plot.survfit and the fun = "cloglog" option), as well as a data set containing that information.

Author(s)

Sarah R. Haile

References

Klein, J. and Moeschberger, M. (2003). Survival analysis: techniques for censored and truncated data. Springer.

See Also

Requires packages survival and prodlim. A similar plot can be produced using plot.survfit and the option fun = "cloglog".

Examples

if (requireNamespace("prodlim")) {
    data(larynx)
    fm <- 
    weibullDiag(formula = Surv(time, death) ~ stage, data = larynx,
                labels = c("Stage I", "Stage II","Stage III", "Stage IV"))
}

felix-hof/biostatUZH documentation built on Sept. 27, 2024, 1:48 p.m.