Calculate lifetime risk of adverse outcomes accounting for competing risks.

• Person-year calculations
• Age-specific incidence rates
• Cumulative incidence with competing risks
• Age-adjusted rates
• Flexible group analysis (overall or subgroups)
• Scalable comparisons (any number of groups)
• Publication-quality plots
• Export results to CSV
• Thorough test suite for reliability

You can install the development version of lifetimeRisk from GitHub with:

# install.packages("remotes")
remotes::install_github("msegar/lifetimeRisk")

Your data should contain:

• ids: Unique identifier for each individual
• entryage: Age at which the individual entered the study
• survage: Age at which the individual was last observed
• status: Primary event indicator (1 if occurred, 0 if not)
• astatus: Competing event indicator (1 if occurred, 0 if not)
• Grouping variables (optional): Any variables for subgroup analysis

Edge cases:

• All columns must be present and correctly named
• No missing values in required columns
• Ages should be numeric and within the analysis range

library(lifetimeRisk)
library(data.table)

set.seed(123)
n <- 1000
sample_data <- data.frame(
  ids = 1:n,
  entryage = round(runif(n, min = 45, max = 65)),
  followup_time = round(runif(n, min = 2, max = 20)),
  status = rbinom(n, 1, 0.3),
  astatus = rbinom(n, 1, 0.2),
  sex = sample(c("Male", "Female"), n, replace = TRUE),
  race = sample(c("White", "Black", "Hispanic", "Asian"), n, replace = TRUE, 
                prob = c(0.6, 0.2, 0.15, 0.05))
)
sample_data$survage <- sample_data$entryage + sample_data$followup_time
sample_data$status <- ifelse(sample_data$astatus == 1, 0, sample_data$status)
sample_data <- sample_data[, c("ids", "entryage", "survage", "status", "astatus", "sex", "race")]

result_overall <- pie_analysis(
  data = sample_data,
  min_age = 45,
  max_age = 90,
  age_group_width = 5,
  group_var = NULL,
  age_free = 45
)
summary(result_overall)

result_sex <- pie_analysis(
  data = sample_data,
  min_age = 45,
  max_age = 90,
  age_group_width = 5,
  group_var = "sex",
  group_levels = c("Male", "Female"),
  age_free = 45
)
summary(result_sex)

result_race <- pie_analysis(
  data = sample_data,
  min_age = 45,
  max_age = 90,
  age_group_width = 5,
  group_var = "race",
  group_levels = NULL,
  age_free = 45
)
summary(result_race)

library(ggplot2)
p <- plot_lifetime_risk(result_sex, adjusted = TRUE, label_names = c("Male" = "Men", "Female" = "Women"))
print(p)

create_lifetime_csv(result_sex, adjusted = TRUE, output_file = "lifetime_risk_sex.csv")

lt_overall <- create_lifetime_risk_table(
  data = sample_data,
  index_ages = c(45, 55, 65),
  max_age = 90,
  group_var = NULL
)
print(lt_overall)

res <- calculate_disease_free_survival(
  data = sample_data,
  time_event_col = "survage",
  time_death_col = "survage",
  event_col = "status",
  death_col = "astatus",
  index_age = 45,
  n_bootstrap = 100
)
print(res)

• pie_analysis(): Main function for person-year and lifetime risk analysis (overall or by group)
• create_person_year_data(): Expand data to person-year format for analysis
• calculate_age_specific_rates(): Compute age-specific incidence rates
• calculate_cumulative_incidence(): Compute cumulative incidence and confidence intervals
• create_lifetime_risk_table(): Summarize lifetime risk from different starting ages
• plot_lifetime_risk(): Plot cumulative incidence curves with confidence intervals
• create_lifetime_csv(): Export lifetime risk estimates to CSV
• calculate_disease_free_survival(): Estimate disease-free and overall survival (restricted mean)
• get_number_at_risk_from_analysis(): Extract number at risk at specific ages from analysis

A comprehensive test suite is provided in tests/testthat/. Run all tests with:

devtools::test()

• All statistical results are validated against the original SAS PIE macro.
• Bootstrapping uses a fixed seed for reproducibility.
• Extensive tests cover edge cases and regression.

• See function documentation via ?function_name in R.
• For detailed examples, see the vignettes and test files.

If you use lifetimeRisk in your research, please cite:

Segar, MW. (2025). lifetimeRisk: R package to calculate lifetime risk of adverse outcomes [Computer software]. Zenodo. https://doi.org/10.5281/zenodo.17117439

See LICENSE file for details.