Print method for mcc summary objects
Usage
# S3 method for class 'summary.mcc'
print(x, ...)Examples
# Attach dplyr
library(dplyr)
# Create sample data with recurrent events
df <- data.frame(
id = c(1, 2, 3, 4, 4, 4, 4, 5, 5),
time = c(8, 1, 5, 2, 6, 7, 8, 3, 3),
cause = c(0, 0, 2, 1, 1, 1, 0, 1, 2)
) |>
arrange(id, time) # Sort the data by id and time
# Calculate MCC using the equation method (default)
mcc_eq <- mcc(df, id_var = "id", time_var = "time", cause_var = "cause")
#> ℹ Adjusted time points for events occurring simultaneously for the same subject.
summary(mcc_eq)
#>
#> ── Summary of Mean Cumulative Count Results ────────────────────────────────────
#> ℹ Method: Dong-Yasui Equation Method
#> ℹ Total participants: 5
#>
#> ── Summary Statistics ──
#>
#> Observation period: [0, 8]
#> Time to MCC = 1.0: 7
#> Time to maximum MCC: 7
#> MCC at end of follow-up: 1
#>
#> ── Event Count Composition
#> Events of interest: 4
#> Competing risk events: 2
#> Censoring events: 3
# Calculate MCC using the sum of cumulative incidence method
mcc_sci <- mcc(
df,
id_var = "id",
time_var = "time",
cause_var = "cause",
method = "sci"
)
#> ℹ Adjusted time points for events occurring simultaneously for the same subject.
print(summary(mcc_sci))
#>
#> ── Summary of Mean Cumulative Count Results ────────────────────────────────────
#> ℹ Method: Sum of Cumulative Incidence Method
#> ℹ Total participants: 5
#>
#> ── Summary Statistics ──
#>
#> Observation period: [0, 8]
#> Time to MCC = 1.0: 7
#> Time to maximum MCC: 7
#> MCC at end of follow-up: 1
#>
#> ── Event Count Composition
#> Events of interest: 4
#> Competing risk events: 2
#> Censoring events: 3
# Clean up
rm(df, mcc_eq, mcc_sci)