summary.lyl_ci: Summarize Life Years Lost with confidence intervals

Description

summary for objects of class lyl_ci summarizes Life Years Lost at one specific age or over a range of different ages, including bootstrapped confidence intervals

Usage

# S3 method for lyl_ci
summary(
  object,
  decimals = 2,
  level = 0.95,
  weights = NA,
  difference = FALSE,
  ...
)

Value

A table with the summary of the results.

Arguments

object: An object of class lyl_ci (obtained with function lyl_ci).
decimals: Number of decimals to be reported (default is 2).
level: Confidence level (default is 0.95 for 95% confidence intervals)
weights: Vector with age distribution of disease/condition onset to be used when Life Years Lost are estimated over a range of ages (with lyl_range function). If weights are not provided (dafault is weights = NA), then the differences in Life Years Lost at each age is provided. If weights are provided, then a weighted average is provided.
difference: Parameter automatically created by the package.
...: Additional arguments affecting the summary produced.

References

Plana-Ripoll et al. lillies – An R package for the estimation of excess Life Years Lost among patients with a given disease or condition. PLoS ONE. 2020;15(3):e0228073.

Examples

Run this code

# Load simulated data as example
data(simu_data)

# Estimate remaining life expectancy and Life Years
# Lost after age 45 years and before age 95 years
lyl_estimation <- lyl(data = simu_data, t = age_death, status = death,
                      age_specific = 45, tau = 95)

# Calculate bootstrapped confidence interval (3 iterations to test; more are necessary)
lyl_estimation_ci <- lyl_ci(lyl_estimation, niter = 3)
summary(lyl_estimation_ci)

# \donttest{
# Estimate remaining life expectancy and Life Years
# Lost after each age from 0 to 94 years and before age 95 years
lyl_estimation2 <- lyl_range(data = simu_data, t = age_death, status = death,
                             age_begin = 0, age_end = 94, tau = 95)

# Calculate bootstrapped confidence interval (3 iterations to test; more are necessary)
lyl_estimation_ci2 <- lyl_ci(lyl_estimation2, niter = 3)
summary(lyl_estimation_ci2, weights = simu_data$age_disease)
# }