tipse (version 1.2)

plot.tipse: Plot Pooled Kaplan–Meier Curves from Tipping Point Analysis

Description

Visualizes averaged (pooled) Kaplan-Meier survival curves across multiple tipping point parameters, highlighting the tipping point where the upper CL of the hazard ratio crosses 1.

Usage

# S3 method for tipse
plot(x, type = c("Kaplan-Meier", "Tipping Point"), ...)

Value

A ggplot2 object displaying pooled Kaplan–Meier curves.

Arguments

x

An S3 object of class "tipse" returned from tipping_point_model_free or tipping_point_model_based.

type

Type of plot, either "Kaplan-Meier" or "Tipping Point".

...

Additional arguments not used.

Details

• If type = Kaplan-Meier, then the KM curves from multiply imputed datasets were pooled using Rubin’s rules after complementary log-log transformation as described in Marshall et al. (2009). it can be of interest to visually assess the scenario that tips the result and the shift it causes to the original KM curve, although there is no objective measure to assess the robustness of the result.

• If type = Tipping Point, then the HR estimation across the range of tipping point parameters are plotted.

References

Marshall, A., Altman, D.G., Holder, R.L. et al. Combining estimates of interest in prognostic modelling studies after multiple imputation: current practice and guidelines. BMC Med Res Methodol 9, 57 (2009). https://doi.org/10.1186/1471-2288-9-57

Examples

cox1 <- survival::coxph(Surv(AVAL, EVENT) ~ TRT01P, data = codebreak200)
result <- tipping_point_model_based(
  dat = codebreak200,
  reason = "Early dropout",
  impute = "docetaxel",
  imputation_model = "weibull",
  J = 10,
  tipping_range = seq(0.1, 1, by = 0.05),
  cox_fit = cox1,
  verbose = TRUE,
  seed = 12345
)
plot(result, type = "Kaplan-Meier")
plot(result, type = "Tipping Point")