Extract MCC estimates from mcc objects
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
mcc_result <- mcc(df, "id", "time", "cause")
#> 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.
# Extract MCC estimates
estimates <- mcc_estimates(mcc_result)
print(estimates)
#> # A tibble: 5 × 2
#> time mcc
#> <dbl> <dbl>
#> 1 0 0
#> 2 2 0.25
#> 3 3 0.5
#> 4 6 0.75
#> 5 7 1
# For grouped analysis
df_grouped <- df |>
mutate(group = c("A", "A", "B", "B", "B", "B", "A", "A"))
mcc_grouped <- mcc(df_grouped, "id", "time", "cause", by = "group")
#> ℹ Adjusted time points for events occurring simultaneously for the same subject.
#> 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
estimates_grouped <- mcc_estimates(mcc_grouped)
print(estimates_grouped)
#> # A tibble: 6 × 3
#> group time mcc
#> * <chr> <dbl> <dbl>
#> 1 A 0 0
#> 2 A 3 0.5
#> 3 B 0 0
#> 4 B 2 0.5
#> 5 B 6 1
#> 6 B 7 1.5
# Clean up
rm(df, df_grouped, mcc_result, mcc_grouped, estimates, estimates_grouped)