Check if mcc object uses weighted estimation
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 unweighted MCC
mcc_unweighted <- 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.
is_weighted(mcc_unweighted) # FALSE
#> [1] FALSE
# Create weighted data
df_weighted <- df |>
group_by(id) |>
slice(1) |>
ungroup() |>
mutate(weights = runif(n(), 0.5, 2.0)) |>
select(id, weights) |>
right_join(df, by = "id") |>
arrange(id, time)
# Calculate weighted MCC
mcc_weighted <- mcc(df_weighted, "id", "time", "cause", weights = "weights")
#> 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.
is_weighted(mcc_weighted) # TRUE
#> [1] TRUE
# Clean up
rm(df, df_weighted, mcc_unweighted, mcc_weighted)