Package 'shapleyHALE'

Example age group information

Description

Matches the age groups in demo_gbd.

Usage

age_info_demo

Format

A data.table with columns:

age_id

numeric age group ID

age_start

start of interval

width

interval width (years)

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Parametric bootstrap Shapley decomposition

Description

Performs bootstrap uncertainty analysis by sampling rates from triangular distributions defined by point estimates and 95

Usage

bootstrap_shapley(
  deaths_data,
  ylds_data,
  base_year,
  target_year,
  cause_ids,
  age_ids,
  ages_info,
  n_boot = 1000,
  n_perm_inner = 5000,
  parallel = FALSE,
  n_cores = NULL
)

Arguments

deaths_data	data.table with mortality rates (cols: year, cause_id, age_id, val, lower, upper)
ylds_data	data.table with YLD rates (same structure)
base_year	baseline year
target_year	target year
cause_ids	character vector of cause IDs
age_ids	numeric vector of age IDs in correct order
ages_info	data.frame with age_start and width, matching age_ids
n_boot	number of bootstrap iterations (default 1000)
n_perm_inner	number of Shapley permutations per bootstrap (default 5000)
parallel	logical, use parallel processing?
n_cores	number of cores (NULL = detectCores()-1)

Value

list containing point estimates and bootstrap summaries

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Compute health-adjusted life expectancy

Description

Sullivan method combining mortality and YLD rates.

Usage

compute_hale(mx, yld_rate, ages)

Arguments

mx	numeric vector of age-specific mortality rates (per person-year)
yld_rate	numeric vector of YLD rates (per person-year)
ages	data.frame with columns 'age_start' and 'width'

Value

list containing HALE0 and LE0

Examples

ages <- data.frame(age_start = c(0,1,5), width = c(1,4,5))
compute_hale(c(0.01,0.02,0.05), c(0.1,0.1,0.1), ages)

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Second-order Shapley interaction indices

Description

Quantifies pairwise synergies/antagonisms among the leading causes.

Usage

compute_shapley_interactions(
  base_mx,
  base_yld,
  target_mx,
  target_yld,
  causes,
  ages,
  top_n = 20,
  n_perm = 1000,
  point_est = NULL
)

Arguments

base_mx	matrix, baseline mortality rates
base_yld	matrix, baseline YLD rates
target_mx	matrix, target mortality rates
target_yld	matrix, target YLD rates
causes	character vector of all cause IDs
ages	data.frame with age_start and width
top_n	number of leading causes to consider (default 20)
n_perm	number of permutations (default 1000)
point_est	optional named vector of total Shapley values (for ranking)

Value

data.table with columns cause_A, cause_B, interaction

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Create cause-rate matrix from GBD-style data.table

Description

Create cause-rate matrix from GBD-style data.table

Usage

create_rate_matrix(data, year_val, cause_ids, age_ids)

Arguments

data	data.table with columns year, cause_id, age_id, val
year_val	numeric year
cause_ids	character vector of cause IDs
age_ids	numeric vector of age IDs in correct order

Value

matrix (ages x causes)

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Demo GBD rates (simulated)

Description

A small simulated dataset mimicking GBD cause-specific mortality and YLD rates.

Usage

demo_gbd

Format

A data.table with columns:

measure_id

1 = mortality, 3 = YLD

sex_id

3 = both sexes

year

1990 or 2023

cause_id

character cause identifier

cause_name

cause name

age_id

numeric age group ID

val

point estimate (rate)

upper

upper 95% UI

lower

lower 95% UI

Source

Simulated for package examples.

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Shapley decomposition of HALE change

Description

Decomposes the HALE change between baseline and target into additive contributions of individual causes, separating mortality and disability effects.

Usage

run_shapley_decomp(
  base_mx,
  base_yld,
  target_mx,
  target_yld,
  causes = NULL,
  ages,
  n_perm = 5000
)

Arguments

base_mx	matrix of baseline mortality rates (age x cause)
base_yld	matrix of baseline YLD rates (age x cause)
target_mx	matrix of target mortality rates
target_yld	matrix of target YLD rates
causes	character vector of cause IDs (column names)
ages	data.frame with 'age_start' and 'width'
n_perm	number of random permutations (default 5000)

Value

list with total_change, total_effect, death_effect, disability_effect

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