Compares two mcc objects and returns a summary of differences.
Useful for comparing results from different methods or parameter settings.
Examples
# Create sample data
library(dplyr)
df <- data.frame(
id = c(1, 2, 3, 4, 4, 4, 5, 5),
time = c(8, 1, 5, 2, 6, 7, 3, 3),
cause = c(0, 0, 2, 1, 1, 1, 1, 2)
) |>
arrange(id, time)
# Calculate MCC using different methods
mcc_eq <- mcc(df, "id", "time", "cause", method = "equation")
#> Warning: Found 1 participant where last observation is an event of interest (`cause_var`
#> = 1)
#> ! ID: 4
#> ℹ `mcc()` assumes these participants are censored at their final `time_var`
#> ℹ If participants were actually censored or experienced competing risks after
#> their last event, add those observations to ensure correct estimates
#> ℹ Adjusted time points for events occurring simultaneously for the same subject.
mcc_sci <- mcc(df, "id", "time", "cause", method = "sci")
#> Warning: Found 1 participant where last observation is an event of interest (`cause_var`
#> = 1)
#> ! ID: 4
#> ℹ `mcc()` assumes these participants are censored at their final `time_var`
#> ℹ If participants were actually censored or experienced competing risks after
#> their last event, add those observations to ensure correct estimates
#> ℹ Adjusted time points for events occurring simultaneously for the same subject.
# Compare the results
comparison <- compare_mcc(mcc_eq, mcc_sci)
print(comparison)
#>
#> ── MCC Object Comparison ───────────────────────────────────────────────────────
#> ! Objects differ in one or more aspects
#>
#> ── Comparison Details ──
#>
#> ✖ Methods match
#> ✔ Weighted status matches
#> ✔ Grouping status matches
#> ✔ Grouping variables match
#> ✔ Final values are close
#> Maximum difference in final values: 0
# Clean up
rm(df, mcc_eq, mcc_sci, comparison)