WeibullDiag: Diagnostic Plot of Adequacy of Weibull Distribution
In biostatUZH: Misc Tools of the Department of Biostatistics, EBPI, University of Zurich

Description Usage Arguments Details Value Author(s) References See Also Examples

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.

1	WeibullDiag(formula, data = parent.frame(), labels = rownames(np$X))

`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.

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 λ + α \log t.

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.

Sarah R. Haile

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

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

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