lyl: Life Years Lost at one specific age.

Description

lyl estimates remaining life expectancy and Life Years Lost for a given population after a specific age age_speficic and restrictied to a maximum theoretical age \(\tau\).

Usage

lyl(data, t0 = NULL, t, status, age_specific,
  censoring_label = "Alive", death_labels = "Dead", tau = 100)

Arguments

data

A dataframe, where each raw represents a person. The dataframe will have a time-to-event format with at least two variables: age at end of follow-up (t) and status indicator with death/censoring (status).

Age at start of the follow-up time. Default is NULL, which means all subjects are followed from birth. For delayed entry, t0 indicates age at beginning of follow-up.

Age at the end of the follow-up time (death or censoring).

status

Status indicator, normally 0=alive, 1=dead. Other choices are TRUE/FALSE (TRUE = death) or 1/2 (2=death). For multiple causes of death (competing risks analysis), the status variable will be a factor, whose first level is treated as censoring; or a numeric variable, whose lowest level is treated as censoring. In the latter case, the label for censoring is censoring_label ("Alive" by default).

age_specific

Specific age at which the Life Years Lost have to be estimated.

censoring_label

Label for censoring status ("Alive" by default).

death_labels

Label for event status. For only one cause of death, "Dead" is the default. For multiple causes, the default are the values given in variable status.

tau

Remaining life expectancy and Life Years Lost are estimated restrictied to a maximum theoretical age \(\tau\) (\(\tau\)=100 years by default).

Value

A list with class "lyl" containing the following components:

data: Data frame with 3 variables and as many observations as the original data provided to estimate Life Years Lost: t0, t, and status
LYL: Data frame with 1 observation and at least 3 variables: age which corresponds to age_spefific; life_exp which is the estimated remaining life expectancy at age age_specific years and before age tau years; and one variable corresponding to the estimated Life Years Lost for each specific cause of death. If only one cause of death is considered (no competing risks), this variable is Dead and includes the total overall Life Years Lost
tau: Maximum theoretical age \(\tau\)
age_specific: Specific age at which the Life Years Lost have been estimated
data_plot: A data frame in long format with 3 variables time, cause, and cip used to create a Figure of Life Years Lost with function plot.
censoring_label: Label for censoring status
death_labels: Label(s) for death status
competing_risks: Logical value (TRUE = more than one cause of death (competing risks))
type: Whether the estimation is at "age_specific" or "age_range".

References

Andersen PK. Life years lost among patients with a given disease. Statistics in Medicine. 2017;36(22):3573- 3582.
Andersen PK. Decomposition of number of life years lost according to causes of death. Statistics in Medicine. 2013;32(30):5278-5285.

Examples

Run this code

# NOT RUN {
# 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)

# Summarize and plot the data
summary(lyl_estimation)
plot(lyl_estimation)

# Estimate remaining life expectancy and Life Years
# Lost due to specific causes of death after age 45
# years and before age 95 years
lyl_estimation2 <- lyl(data = simu_data, t = age_death, status = cause_death,
                       age_specific = 45, tau = 95)

# Summarize and plot the data
summary(lyl_estimation2)
plot(lyl_estimation2)

# }

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