Survival times for 205 patients with malignant melanoma after surgery. This rich clinical dataset includes multiple covariates and heavy censoring.
Format
A data frame with 205 rows and 7 columns:
- time
Survival time in days
- status
Event indicator (1 = died from melanoma, 0 = alive, 2 = other death)
- thickness
Tumor thickness in mm
- sex
Patient sex (1 = male, 0 = female)
- age
Patient age in years
- ulcer
Ulceration indicator (1 = present, 0 = absent)
- year
Year of operation
Source
Andersen, P. K., Borgan, O., Gill, R. D., & Keiding, N. (1993). Statistical Models Based on Counting Processes. Springer.
Examples
data(melanoma)
# \donttest{
# Treat status 1 as event, others as censored
melanoma$event <- ifelse(melanoma$status == 1, 1, 0)
fit <- fit_betadanish(survival::Surv(time, event) ~ age + thickness, data = melanoma)
#> Warning: The times look recorded on a grid of 1. The point-density likelihood treats them as exact, which understates the standard errors. Consider grouped = TRUE.
#> Warning: The fitted correlation between a-hat and c-hat is -0.959, so effectively only the product c*a is identified. Individual estimates of a and c should not be interpreted.
#> Warning: 7 starting point(s) reached a degenerate ridge and were discarded. The reported fit is the best admissible optimum. If this is most of the grid, the four-parameter model is a poor choice for these data.
# }