Extract calculation details 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 with details
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.
# Extract calculation details
details_eq <- mcc_details(mcc_eq) # Returns mcc_table
details_sci <- mcc_details(mcc_sci) # Returns sci_table
print(details_eq)
#> # A tibble: 9 × 8
#> time nrisk censor event cmprk overall_surv_previous ave_events mcc
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 0 5 0 0 0 1 0 0
#> 2 1 5 1 0 0 1 0 0
#> 3 2 4 0 1 0 1 0.25 0.25
#> 4 3 4 0 1 0 1 0.25 0.5
#> 5 3.00 4 0 0 1 1 0 0.5
#> 6 5 3 0 0 1 0.75 0 0.5
#> 7 6 2 0 1 0 0.5 0.25 0.75
#> 8 7 2 1 1 0 0.5 0.25 1
#> 9 8 1 1 0 0 0.5 0 1
print(details_sci)
#> # A tibble: 9 × 5
#> time CI1 CI2 CI3 SumCIs
#> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 0 0 0 0 0
#> 2 1 0 0 0 0
#> 3 2 0.25 0 0 0.25
#> 4 3 0.5 0 0 0.5
#> 5 3.00 0.5 0 0 0.5
#> 6 5 0.5 0 0 0.5
#> 7 6 0.5 0.25 0 0.75
#> 8 7 0.5 0.25 0.25 1
#> 9 8 0.5 0.25 0.25 1
# Clean up
rm(df, mcc_eq, mcc_sci, details_eq, details_sci)