plot_transformed_km: Plot to assess suitability of parametric model

In survHE: Survival Analysis in Health Economic Evaluation

Plot to assess suitability of parametric model

Description

Perform an exploratory investigation for linearity of transformed survival models.

Usage

plot_transformed_km(fit, mod = 1, add_legend = FALSE, graph = "base", ...)

Arguments

fit	An object of class survHE.
mod	Index or name of a model in fit. Defaults to 1.
add_legend	If TRUE, labels assumptions. Defaults to FALSE.
graph	Type of plot: base or ggplot2.
...	Further arguments, passed on to plot.

Details

For the Weibull, twice taking logs of the survivor function

log(-log S(t)) = log \lambda + \gamma log t

A plot of log(-log S(t)) against log(t) would give an approximately straight line if the Weibull assumption is reasonable. The plot could also be used to give a rough estimate of the parameters.

Similarly, for the log-logistic distribution

logS(t)/(1 - S(t)) = \theta - \kappa log t

For the log-normal distribution

\Phi^{-1} (1 - S(t)) = (log t - \mu) / \sigma

We can also check the assumption made with using the Cox regression model of proportional hazards by inspecting the log-cumulative hazard plot.

log H_i(t) = \beta x_i + log H_0(t)

The transformed curves for different values of the explanatory variables will be parallel if PH holds.

Value

Diagnostic plot

Author(s)

William Browne, Nathan Green

References

Collett (2015) Modelling Survival Data in Medical Research, CRC Press

Examples

data(bc)
form <- formula("Surv(recyrs, censrec) ~ group")

# exponential distribution
fit_exp <- fit.models(form, data = bc,
                  distr = "exp", method = "mle")
plot_transformed_km(fit_exp)
plot_transformed_km(fit_exp, graph = "ggplot2")
                 
# weibull distribution
fit_wei <- fit.models(form, data = bc,
                  distr = "weibull", method = "mle")
plot_transformed_km(fit_wei)
plot_transformed_km(fit_wei, graph = "ggplot2")
                 
# loglogistic distribution
fit_llog <- fit.models(form, data = bc,
                  distr = "loglogistic", method = "mle")
plot_transformed_km(fit_llog)
plot_transformed_km(fit_llog, graph = "ggplot2")
                 
# lognormal distribution
fit_lnorm <- fit.models(form, data = bc,
                  distr = "lognormal", method = "mle")
plot_transformed_km(fit_lnorm)
plot_transformed_km(fit_lnorm, graph = "ggplot2")

## for only one group
form <- formula("Surv(recyrs, censrec) ~ 1")

fit_exp <- fit.models(form, data = bc,
                  distr = "exp", method = "mle")
plot_transformed_km(fit_exp)
plot_transformed_km(fit_exp, graph = "ggplot2")

survHE documentation built on March 31, 2023, 11:37 p.m.